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  1. 101

    Using de novo assembly to identify structural variation of eight complex immune system gene regions. by Jia-Yuan Zhang, Hannah Roberts, David S C Flores, Antony J Cutler, Andrew C Brown, Justin P Whalley, Olga Mielczarek, David Buck, Helen Lockstone, Barbara Xella, Karen Oliver, Craig Corton, Emma Betteridge, Rachael Bashford-Rogers, Julian C Knight, John A Todd, Gavin Band

    Published 2021-08-01
    “…Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. …”
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  2. 102

    Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations by Seunghwan Lee, Seungjoon Eom, Jinsu Jeong, Junjong Lee, Sanguk Lee, Hyeok Yun, Yonghwan Ahn, Rock-Hyun Baek

    Published 2024-01-01
    “…We present a novel multi-task learning (MTL) approach with shared representation for the real-time extraction of Berkeley Short-channel IGFET Model-Common Gate (BSIM-CMG) parameters in nanosheet field-effect transistors (NSFETs) with multiple structural variations. An innovative artificial neural network (ANN) architecture, coupled with specialized training strategies, was introduced to extract BSIM-CMG parameters in NSFETs with varying gate lengths (<inline-formula> <tex-math notation="LaTeX">$L_{\mathrm {g}}$ </tex-math></inline-formula>), nanosheet widths (<inline-formula> <tex-math notation="LaTeX">$W_{\mathrm {ns}}$ </tex-math></inline-formula>), and thicknesses (<inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {ns}}$ </tex-math></inline-formula>). …”
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  3. 103

    Insights into the Structural and Nutritional Variations in Soluble Dietary Fibers in Fruits and Vegetables Influenced by Food Processing Techniques by Wenjie Sui, Shuiqing Wang, Yue Chen, Xiaoxuan Li, Xin Zhuang, Xinhuan Yan, Ye Song

    Published 2025-05-01
    “…Processing-induced structure variations in SDFs inevitably change their fermentability and gelling ability, promote the growth of beneficial bacteria and the production of short-chain fatty acids, enhance immunity, and reduce the risk of chronic diseases. …”
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  4. 104
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    Multifractal Analysis of Temporal Variation in Soil Pore Distribution by Yanhui Jia, Yayang Feng, Xianchao Zhang, Xiulu Sun

    Published 2024-12-01
    “…This study employs multifractal analysis to assess the temporal variation in soil pore distribution, a pivotal factor in soil structure. …”
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    Warped matrix-variate Gaussian processes with structured kernels for multi-step-ahead driving behaviour prediction by Seiya Takano, Tomohiko Jimbo

    Published 2025-12-01
    “…This study proposes a novel probabilistic model called the matrix-variate Gaussian process (GP) with structured kernels while focusing on the periodicity. …”
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    Along‐arc variation in the 3‐D thermal structure around the junction between the Japan and Kurile arcs by Manabu Morishige, Peter E. van Keken

    Published 2014-06-01
    “…Abstract The thermal structure in subduction zones has a strong influence on seismogenesis and arc volcanism. …”
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  14. 114

    Genetic variation and structure shaped by recent population fragmentation in the boreal conifer Thuja koraiensis: Conservation perspectives by Eun-Kyeong Han, Ichiro Tamaki, Tae-Im Heo, Jun-Gi Byeon, Amarsanaa Gantsetseg, Young-Jong Jang, Jong-Soo Park, Jung-Hyun Lee

    Published 2025-06-01
    “…We used a genome dataset (242 SNPs) generated by the MIG-seq (Multiplexed ISSR Genotyping by Sequencing) method to evaluate the genetic diversity and structure of T. koraiensis populations across their entire distribution range. …”
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  15. 115

    Predicting rare drug-drug interaction events with dual-granular structure-adaptive and pair variational representation by Zhonghao Ren, Xiangxiang Zeng, Yizhen Lao, Zhuhong You, Yifan Shang, Quan Zou, Chen Lin

    Published 2025-04-01
    “…Here we introduce RareDDIE, a metric-based meta-learning model that employs a dual-granular structure-driven pair variational representation to enhance rare DDIE prediction. …”
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  16. 116

    Clonality, spatial structure, and pathogenic variation in Fusarium fujikuroi from rain-fed rice in southern Laos. by Barbara Scherm, Virgilio Balmas, Alessandro Infantino, Maria Aragona, Maria Teresa Valente, Francesca Desiderio, Angela Marcello, Sengphet Phanthavong, Lester W Burgess, Domenico Rau

    Published 2019-01-01
    “…In this study, we investigated the population structure of this fungus in southern Lao PDR, a country located near the geographic origin of rice domestication. …”
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  17. 117

    The impact of quantum circuit architecture and hyperparameters on variational quantum algorithms exemplified in the electronic structure of the GaAs crystal by Ivana Miháliková, Michal Krejčí, Martin Friák

    Published 2025-05-01
    “…Abstract Variational Quantum Algorithms (VQAs) provide a promising framework for solving electronic structure problems using the computational capabilities of quantum computers to explore high-dimensional Hilbert spaces efficiently. …”
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