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Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk (vol 54, pg 804, 2022)

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NATURE GENETICS 2022年第8期54卷 1259-1259页

作者： Turner, Adam W. Hu, Shengen Shawn Mosquera, Jose Verdezoto Ma, Wei Feng Hodonsky, Chani J. Wong, Doris Auguste, Gaelle Song, Yipei Sol-Church, Katia Farber, Emily Kundu, Soumya Kundaje, Anshul Lopez, Nicolas G. Ma, Lijiang Ghosh, Saikat Kumar B. Onengut-Gumuscu, Suna Ashley, Euan A. Quertermous, Thomas Finn, Aloke V. Leeper, Nicholas J. Kovacic, Jason C. Bjorkegren, Johan L. M. Zang, Chongzhi Miller, Clint L. Center for Public Health Genomics University of Virginia Charlottesville VA USA Department of Biochemistry and Molecular Genetics University of Virginia Charlottesville VA USA Medical Scientist Training Program University of Virginia Charlottesville VA USA Department of Pathology University of Virginia Charlottesville VA USA Robert M. Berne Cardiovascular Research Center University of Virginia Charlottesville VA USA Genome Sciences Laboratory University of Virginia Charlottesville VA USA Department of Public Health Sciences University of Virginia Charlottesville VA USA Genome Analysis & Technology Core University of Virginia Charlottesville VA USA Department of Genetics Stanford University School of Medicine Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Division of Cardiovascular Medicine Department of Medicine Stanford University Stanford CA USA Division of Vascular Surgery Department of Surgery Stanford University Stanford CA USA Department of Genetics and Genomic Sciences Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Integrated Cardio Metabolic Centre Department of Medicine Karolinska Institutet Huddinge Sweden CVPath Institute Gaithersburg MD USA Cardiovascular Research Institute Icahn School of Medicine at Mount Sinai New York NY USA Victor Chang Cardiac Research Institute Darlinghurst New South Wales Australia St. Vincent’s Clinical School University of New South Wales Sydney New South Wales Australia

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impactingcis-regulatory elements. Here, we applied single-nucleus assay for transposase-accessible chromatin with sequencing to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell-type-specific elements and transcription factors, and prioritized functional CAD risk variants. We identified elements in smooth muscle cell transition states (for example, fibromyocytes) and functional variants predicted to alter smooth muscle cell- and macrophage-specific regulation ofMRAS(3q22) andLIPA(10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single-nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.

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Neural Architecture Search for Optimization of Spatial-Temporal Brain Network Decomposition 23rd

Neural Architecture Search for Optimization of Spatial-Tempo...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Qing Zhang, Wei Lv, Jinglei Wu, Xia Liu, Tianming School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States Department of Biomedical Engineering University of Melbourne ParkvilleVIC Australia

ISBN: (纸本)9783030597276

Using neural networks to explore spatial patterns and temporal dynamics of human brain activities has been an important yet challenging problem because it is hard to manually design the most optimal neural networks. There have been several promising deep learning methods that can decompose neuroscientifically meaningful spatial-temporal patterns from 4D fMRI data, e.g., the deep sparse recurrent auto-encoder (DSRAE). However, those previous studies still depend on hand-crafted neural network structures and hyperparameters, which are not optimal in various senses. In this paper, we employ evolutionary algorithms to optimize such DSRAE neural networks by minimizing the expected loss of the generated architectures on data reconstruction via the neural architecture search (NAS) framework, named NAS-DSRAE. The optimized NAS-DSRAE is evaluated by the publicly available human connectome project (HCP) fMRI datasets and our promising results showed that NAS-DSRAE has sufficient generalizability to model the spatial-temporal features and is better than the hand-crafted model. To our best knowledge, the proposed NAS-DSRAE is among the earliest NAS models that can extract connectome-scale meaningful spatial-temporal brain networks from 4D fMRI data. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

arXiv

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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A Design of Crowd-Based Corruption Prevention System in Indonesia: Indonesia Corruption Map 4

A Design of Crowd-Based Corruption Prevention System in Indo...

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4th International Conference on Information Management and technology, ICIMTech 2019

作者： Nurlina Kristin, Desi Maya Muqsith, Andi Muhammad Mursitama, Tirta Nugraha Sasmoko Makalew, Brilly Andro Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Bina Nusantara University Jakarta11480 Indonesia Mobile Application Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Combatting corruption requires not only centralized and institution-based strategy, but must be met with distributive effort supported by abundance of data and technological advancement. This work tackles the issue of decentralized corruption prevention by describing the method of doing heuristic analysis based on multiple data regarding corruption. There are four main data source used for the input of the system in this work, where each data corresponds to a city in a region. All the data are processed into corresponding index, while the indexes will be further used in index interpretation and suggestion mapping process. The output of this system is a decision support system for relevant decision making related to corruption prevention, visualized using Google Map API. This system will be very powerful in the long and tedious war against corruption, when used to its maximum potential. © 2019 IEEE.

关键词： Mapping

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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Assessing the memory ability of recurrent neural networks

arXiv

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arXiv 2020年

作者： Zhang, Cheng Li, Qiuchi Hua, Lingyu Song, Dawei Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China Department of Information Engineering University of Padua Padua Italy School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science Beijing Institute of Technology Beijing China

It is known that Recurrent Neural Networks (RNNs) can remember, in their hidden layers, part of the semantic information expressed by a sequence (e.g., a sentence) that is being processed. Different types of recurrent units have been designed to enable RNNs to remember information over longer time spans. However, the memory abilities of different recurrent units are still theoretically and empirically unclear, thus limiting the development of more effective and explainable RNNs. To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence. Based on the Semantic Euclidean Space, a series of evaluation indicators are defined to measure the memory abilities of different recurrent units and analyze their limitations. These evaluation indicators also provide a useful guidance to select suitable sequence lengths for different RNNs during training. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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QCluster: Clustering Packets for Flow Scheduling

arXiv

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arXiv 2020年

作者： Yang, Tong Wang, Hao Li, Jizhou Jiang, Jie Zhao, Yikai Zhang, Yinda Yang, Kaicheng Zhang, Nicholas School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Theory Lab China

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45]. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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Use of a wearable device to compare subjective and objective fatigue in lung cancer patients and cancer-free controls

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European Journal of Oncology Nursing 2024年 70卷 102587-102587页

作者： Chou, Ting-Ling Shih, Chi-Huang Chou, Pai-Chien Lai, Jun-Hung Huang, Tsai-Wei School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan Division of Thoracic Medicine Department of Internal Medicine School of Medicine Taipei Medical University Taipei Taiwan Division of Gastroenterology & Hepatology Department of Internal Medicine Erlin Christian Hospital Changhua Taiwan Cochrane Taiwan Taipei Medical University Taipei Taiwan Research Center in Nursing Clinical Application Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Nursing Wan Fang Hospital Taipei Medical University Taipei Taiwan

Purpose: The study evaluates the use of heart rate variability (HRV), a measure of autonomic nervous system (ANS) modulation via wearable smart bands, to objectively assess cancer-related fatigue (CRF) levels. It aims to enhance understanding of fatigue by distinguishing between LF/HF ratios and LF/HF disorder ratios through HRV and photoplethysmography (PPG), identifying them as potential biomarkers. Methods: Seventy-one lung cancer patients and 75 non-cancer controls wore smart bands for one week. Fatigue was assessed using Brief Fatigue Inventory, alongside sleep quality and daily interference. HRV parameters were analyzed to compare groups. Results: Cancer patients showed higher fatigue and interference levels than controls (64.8% vs. 54.7%). Those with mild fatigue had elevated LF/HF disorder ratios during sleep (40% vs. 20%, P = 0.01), similar to those with moderate to severe fatigue (50% vs. 20%, P = 0.01), indicating more significant autonomic dysregulation. Notably, mild fatigue patients had higher mean LF/HF ratios than controls (1.9 ± 1.34 vs. 1.2 ± 0.6, P = 0.01), underscoring the potential of disorder ratios in signaling fatigue severity. Conclusions: Utilizing wearable smart bands for HRV-based analysis is feasible for objectively assess CRF levels in cancer patients, especially during sleep. By distinguishing between LF/HF ratios and LF/HF disorder ratios, our findings suggest that wearable technology and detailed HRV analysis offer promising avenues for real-time fatigue monitoring. This approach has the potential to significantly improve cancer care by providing new methods for managing and intervening in CRF, particularly with a focus on autonomic dysregulation as a crucial factor. © 2024 Elsevier Ltd

关键词： Cancer-related fatigue Heart rate variability Photoplethysmography Smart band Wearable device

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Digital Audio Watermarking by Amplitude Embedding System 15th

Digital Audio Watermarking by Amplitude Embedding System

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, applications and Tools, FITAT 2019

作者： Lin, Meng-Ju Zhao, Ming Chen, Shuo-Tsung Chen, Ching-Ju Department of Applied Mathematics Tunghai University No. 1727 Sec.4 Taiwan Boulevard Taichung Taiwan40704 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan

ISBN: (纸本)9789811397134

This paper proposed an amplitude embedding system for digital audio watermarking in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then amplitude-based embedding rules are combined into an amplitude-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Karush–Kuhn–Tucker (KKT) Theorem is used to obtain the optimal solution, which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Simulating local fields in carbon nanotube reinforced composites for infinite strip with voids

arXiv

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arXiv 2021年

作者： Nasser, Mohamed M.S. Kalmoun, El Mostafa Mityushev, Vladimir Rylko, Natalia Mathematics Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University Doha Qatar School of Science and Engineering Al Akhawayn University in Ifrane PO Box 104 Ifrane53000 Morocco Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland

We consider the steady heat conduction problem within a thermal isotropic and homogeneous infinite strip composite reinforced by uniformly and randomly distributed non-overlapping carbon nanotubes (CNTs) and containing voids. We treat the CNTs as thin perfectly conducting elliptic inclusions and assume the voids to be of circular shape and act as barriers to heat flow. We also impose isothermal conditions on the external boundaries by assuming the lower infinite wall to be a heater under a given temperature, and the upper wall to be a cooler that can be held at a lower fixed temperature. The equations for the temperature distribution are governed by the two-dimensional Laplace equation with mixed Dirichlet-Neumann boundary conditions. The resulting boundary value problem is solved using the boundary integral equation with the generalized Neumann kernel. We illustrate the performance of the proposed method through several numerical examples including the case of the presence a large number of CNTs and voids. © 2021, CC BY-NC-ND.

关键词： Carbon nanotubes

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