116 research outputs found
Nonspherical particle suspensions in wall turbulence
No full textIn the present PhD thesis, we investigate the dynamics of small non-spherical particles suspended in a fully developed turbulent channel flow. Non-spherical particles are approximated as axisymmetric spheroidal and triaxial ellipsoidal particles, which are characterized by their inertia and shape. As a starting point, the rotational dynamics of an inertial oblate spheroidal particle suspended in a creeping linear shear flow is investigated, which is later used in interpreting the results obtained in complex turbulent flows. The three-dimensional turbulent flow field is obtained from the Navier-Stokes equations by means of direct numerical simulation in an Eulerian reference frame. The particles are tracked using Lagrangian point particle approach. Existing methodology to simulate the prolate spheroidal particles in wall-bounded turbulent channel flow is extended to investigate the dynamics of oblate spheroidal particles and triaxial ellipsoidal particles. The effects of particle inertia, particle shape, and fluid shear on particle rotation and orientation are reported with respect to both fluid and particle reference frame. Particles at the channel center and near the wall show different rotation patterns and surprisingly different effects of particle inertia. Finally, we report the systematic investigation of the gravity force effects on the dynamics of prolate spheroidal particles
Higher anthocyanin accumulation was associated with higher transcription levels of anthocyanin biosynthesis genes in spinach
No full textSpinach (Spinacia oleracea L.) is widely cultivated as an economically important green leafy vegetable crop for fresh and processing consumption. The red purple spinach shows abundant anthocyanin accumulation in the leaf and leaf petiole. However, the molecular mechanisms of anthocyanin synthesis in this species are still undetermined. In the present study, we investigated the pigments formation and identified anthocyanin biosynthetic genes in spinach, and performed the expression analysis of anthocyanin related genes in the purple and green cultivar by quantitative PCR. Results showed that accumulation of anthocyanin was the dominant pigment resulting in the red coloration in spinach, and 22 biosynthesis genes and 25 regulatory genes were identified in spinach, based on the spinach genomic and transcriptomic database. Furthermore, the expression patterns of genes encoding enzymes indicated that SoPAL, SoUFGT3 and SoUFGT4 were possible candidate genes for anthocyanin biosynthesis in red purple spinach. The expression patterns of transcription factors indicated that two SoMYBs, three SobHLHs and one SoWD40 were drastically up-regulated and co-expression in red purple spinach, suggesting an essential role of regulatory genes in the anthocyanin biosynthesis of spinach. The results above enhanced our understanding about the molecular mechanisms of anthocyanin biosynthesis in purple spinach.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
Supervised and unsupervised learning for 3D brain image segmentation
No full textM.Phil.Supervised and unsupervised automatic brain structure segmentation play pivotal roles in diagnosing brain diseases, including brain tumors, neuropsychiatric disorders and so on, thus are important in the clinical practice. However, developing an automatic approach to segment the brain structures remains very challenging in both supervised and unsupervised learning area. Supervised methods suffer from capturing the detail of complex brain structures, i.e., the ambiguous boundary, complex anatomical structures, and large variance in shapes of brain tissues. While for unsupervised segmentation methods, current registration-based segmenting architectures often fail to capture the hidden mapping relationship from unaligned image to reference image, thereby reducing the discriminativeness of learned features. In this thesis, to alleviate the challenging problems, we propose two deep-learning-based methods for supervised and unsupervised learning, respectively.First of all, we focus on the supervised learning method for 3D brain structure segmentation. We present a novel deep convolutional neural network ( -Net), which aims at boosting the information flow and enhancing the feature propagation. Specifically, we formulate a densely convolutional LSTM module (DCLSTM) to selectively aggregate the convolutional features (with the same spatial resolution) at the same stage of a CNN, which promotes the discriminativeness of features at each CNN stage. By stacking multiple DC-LSTMs among different stages, we can progressively and selectively aggregate the highly semantic features in deep stages to refine the fine detail features in shallow stages for better brain segmentation. Extensive experiments on two public benchmark datasets on sub-cortical brain structure segmentation validates the efficacy and effectiveness of our proposed network.Second, towards unsupervised segmentation, we study segmentation based on registration architecture, which transfers one brain image into the coordinate system of another brain image with the matched imaging contents. By employing this transformation relation between unlabeled images (unaligned images) and one brain volume with annotation (reference image), we can easily transfer the segmentation mask of the reference image to all unlabeled brain images. Specifically, to capture the internal transformation relation between a pair of 3D brain images, we develop a novel probabilistic multilayer regularization network for unsupervised brain image registration and segmentation. We first introduce direct regularizations into the hidden layers of two deep convolutional neural networks (CNNs) by adopting probabilistic models between hidden layers in these two CNNs. Furthermore, we embed the regularization terms into multiple layers of the CNNs and produce the feature-level latent variables in different layers. Combining the predicted feature-level latent variables of all layers, we can obtain a transformation metrics with rich information for a robust 3D brain image registration and segmentation. We employ two common benchmark datasets for unsupervised 3D brain image segmentation. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.監督式和無監督式的自動腦結構分割在診斷腦疾病的過程中起著關鍵作用(例如腦腫瘤, 神經精神疾病等), 因此該類方法在臨床實踐中具有重要意義。然而, 在監督式和無監督式學習領域中, 開發一種自動分割大腦結構的方法仍然非常具有挑戰性。監督式學習的方法難以捕獲复雜的大腦結構的細節, 例如腦溝模糊的邊界, 復雜的解剖結構以及大腦組織形狀的巨大差异。而當前基於配準架構的無監督分割模型通常無法捕獲從未對齊圖像到參考圖像的隱藏映射關系, 從而降低了用於圖像配準和圖像分割的特徵的判别能力。为了解决以上問題, 本文提出了兩種基於深度學習的監督式和無監督式的學習方法。首先, 針對於3D 腦結構分割的監督學習方法, 我們提出了一種新穎的深度卷積神經網絡( -Net) 來促進神經網絡的信息流和特徵傳播。具體來说, 我們設計了密集卷積長短期記憶模塊(DC-LSTM), 該模塊在CNN 的同一階段選擇性地聚合具有相同的空間分辨率的卷積特徵從, 而達到促進了每個CNN階段聚合特徵的判别能力。基於此, 通過在不同CNN 階段之間堆砌多個DC-LSTM, 我們可以逐步有選擇地聚合深層CNN階段的高度語義特徵, 用聚合的特徵來精煉更具精細細節的淺層CNN 階段的特徵, 以此實現更好的腦部分割。我們在兩個關於大腦皮層下結構的公共基準數據集上進行的大量實驗, 實驗結果驗證了我們提出的網絡的有效性和高效性。其次, 對於無監督式的分割方法, 我們研究方向主要針對基於配準架構的分割方法。該類分割方法主要涉及到將一個大腦圖像轉移到另一個大腦圖像的坐標系的配準的過程。通過應用配準過程中的没有分割結果的圖像(未對齊圖像) 和帶有分割結果的圖像(參考圖像) 間的轉化關系, 我們可以輕鬆地將參考圖像的分割結果轉化到所有没有分割結果的大腦圖像上。具體來说, 为了捕獲一對3D 腦圖像之間的内部轉化關系, 我們提出了一種新穎的多層概率正則化模型來對腦圖像進行無監督配準和分割。。我們首先在兩個深度卷積神經網絡的隱藏層之間建立概率模型, 將概率正則化模型直接加入這兩個深度卷積神經網絡的隱藏層。此外, 我們將概率正則化模型項嵌入到CNN的多個階段中, 然後在不同CNN 階段中生成一個特徵級的隱藏變量。結合所有階層預測出來的特徵級的隱藏變量, 我們可以獲得一個具有更豐富轉化信息的轉化指標, 基於此指標來達到更爲魯棒的3D 腦圖像的無監督式配準和分割。我們在兩個常見的基準數據集進行無監督的3D 腦圖像配準和分割。實驗結果表明, 在兩個數據集上, 我們的方法明顯優於最新方法。Liu, Lihao."December 2019."Thesis M.Phil. Chinese University of Hong Kong 2020.Includes bibliographical references (leaves 74-85).Abstracts also in Chinese.Title from PDF title page (viewed on February 17, 2022)
Reliable Performance Characterization of Mediated Photocatalytic Water‐Splitting Half Reactions
No full textReliable Performance Characterization of Mediated Photocatalytic Water-Splitting Half Reactions
No full textPhotocatalytic approaches using two sets of semiconductor particles and a pair of redox shuttle mediators are considered as a safe and economic solution for solar water splitting. Here, we report on accurate experimental characterization techniques for photocatalytic half reactions investigating the gas as well as the liquid products. The method is exemplified utilizing photocatalytic titania particles in an iron-based aqueous electrolyte for effective oxygen evolution and mediator reduction reactions under illumination. Several product characterization methods, including an optical oxygen sensor, pressure sensor, gas chromatography, and UV-Vis spectroscopy are used and compared for accurate, high-resolution gas-products and mediator conversion measurements. Advantages of each technique are discussed. A high Faraday efficiency of 97.5%±2% is calculated and the reaction rate limits are investigated.LRES
Inside Cover: Reliable Performance Characterization of Mediated Photocatalytic Water-Splitting Half Reactions (ChemSusChem 10/2017)
No full textDecoding iron deficiency in cancer: mechanisms, immune modulation, and therapeutic potential
No full textIron is a vital micronutrient in many biological functions, including DNA metabolism, oxygen transport, and cellular energy generation. In this context, it is intimately linked to cancer biology. However, although many studies have comprehensively investigated and reviewed the effects of excess iron on tumor initiation and progression, the potential interrelations of iron deficiency with tumors have been largely neglected and need to be better defined. Recent studies have highlighted the complex relationship between iron deficiency and tumor biology. Iron deficiency in specific tumor types can promote tumor progression through activation of hypoxic responses, metabolic reprogramming, and suppression of the immune response, as well as inhibit tumor growth by limiting tumor cell proliferation, among other mechanisms. This review aims to systematically explore the dual mechanisms of iron deficiency in tumors, its specific effects in different tumor types, its impact on tumor metabolism, immune responses, and therapy, and its prospects as a potential therapeutic target. Furthermore, the potential of iron metabolism markers in tumor diagnosis and prognosis is discussed. By synthesizing existing evidence, this paper comprehensively explains how iron deficiency affects tumorigenesis and identifies future research and clinical practice directions
Research on a Heterogeneous Multi-satellite Mission Scheduling Model for Earth Observation Based on Adaptive Genetic-Tabu Hybrid Search Algorithm
No full text
Stochastic Porous Microstructures
No full textStochastic porous structures are ubiquitous in natural phenomena and have
gained considerable traction across diverse domains owing to their exceptional
physical properties. The recent surge in interest in microstructures can be
attributed to their impressive attributes, such as a high strength-to-weight
ratio, isotropic elasticity, and bio-inspired design principles.
Notwithstanding, extant stochastic structures are predominantly generated via
procedural modeling techniques, which present notable difficulties in
representing geometric microstructures with periodic boundaries, thereby
leading to intricate simulations and computational overhead. In this
manuscript, we introduce an innovative method for designing stochastic
microstructures that guarantees the periodicity of each microstructure unit to
facilitate homogenization. We conceptualize each pore and the interconnecting
tunnel between proximate pores as Gaussian kernels and leverage a modified
version of the minimum spanning tree technique to assure pore connectivity. We
harness the dart-throwing strategy to stochastically produce pore locations,
tailoring the distribution law to enforce boundary periodicity. We subsequently
employ the level-set technique to extract the stochastic microstructures.
Conclusively, we adopt Wang tile rules to amplify the stochasticity at the
boundary of the microstructure unit, concurrently preserving periodicity
constraints among units. Our methodology offers facile parametric control of
the designed stochastic microstructures. Experimental outcomes on 3D models
manifest the superior isotropy and energy absorption performance of the
stochastic porous microstructures. We further corroborate the efficacy of our
modeling strategy through simulations of mechanical properties and empirical
experiments.Comment: 27 pages, 16 figure
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