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The Correlation of 3D DT-MRI Fiber Disruption with Structural and Mechanical Degeneration in Porcine Myocardium

ANNALS OF BIOMEDICAL ENGINEERING 2010年第10期38卷 3084-3095页

作者： Zhang, Song Crow, J. Allen Yang, Xiaoyong Chen, Joseph Borazjani, Ali Mullins, Katie B. Chen, Wei Cooper, Robert C. McLaughlin, Ronald M. Liao, Jun Mississippi State Univ Dept Agr & Biol Engn Cardiovasc Tissue Biomech Lab Mississippi State MS 39762 USA Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Mississippi State Univ Coll Vet Med Ctr Environm Hlth Sci Mississippi State MS 39762 USA Zhejiang Univ State Key Lab CAR & CG Hangzhou 310003 Zhejiang Peoples R China Mississippi State Univ CAVS Mississippi State MS 39762 USA

Evaluation of structural parameters following a myocardial infarction (MI) is important to assess left ventricular function and remodeling. In this study, we assessed the capability of 3D diffusion tensor magnetic resonance imaging (DT-MRI) to assess tissue degeneration shortly after an MI using a porcine model of infarction. Two days after an induced infarction, hearts were explanted and immediately scanned by a 3T MRI scanner with a diffusion tensor imaging protocol. 3D fiber tracks and clustering models were generated from the diffusion-weighted imaging data. We found in a normal explanted heart that DT-MRI fibers showed a multilayered helical structure, with fiber architecture and fiber density reflecting the integrity of muscle fibers. For infarcted heart explants, we observed either a lack of fibers or disruption of fibers in the infarcted regions. Contours of the disrupted DT-MRI fibers were found to be consistent with the infarcted regions. Both histological and mechanical analysis of the infarcted hearts suggested DT-MRI fiber disruption correlated with altered microstructure and tissue mechanics. The ability of 3D DT-MRI to accurately distinguish viable myocardium from dead myocardium only 2 days post infarct without the use of radioisotopes or ionotropic agents makes it a promising approach to evaluate cardiac damage early post-MI.

关键词： Diffusion tensor MRI 3D myocardial fiber disruption Fiber tracks clustering model Cardiac biomechanics

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A novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes from dynamic and static PPI networks

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INFORMATION SCIENCES 2020年 522卷 275-298页

作者： Wang, Rongquan Wang, Caixia Liu, Guixia Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China China Foreign Affairs Univ Sch Int Econ Beijing 100037 Peoples R China

Discovering protein complexes from protein-protein interaction (PPI) networks is one of the primary tasks in bioinformatics. However, most of the state-of-the-art methods still face some challenges, such as the inability to discover overlapping protein complexes, failure to consider the inherent structure of real protein complexes, and non-utilization of biological information. Based on the above mentioned aspects, we present a novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes using a new clustering model in dynamic and static weighted PPI networks (named MPC-C). First, MPC-C constructed dynamic and static weighted PPI networks by combining biological and topological information. Second, initial clusters were obtained using core and multifunctional proteins, following which we proposed a greedy heuristic search algorithm to expand each initial cluster and form candidate protein complexes in dynamic and static weighted PPI networks. Finally, unreliable and highly overlapping protein complexes were discarded. To demonstrate the performance of MPC-C, we tested this method on five PPI networks and compared it with nine other effective methods. The experimental results indicate that MPC-C outperformed the other state-of-the-art methods with respect to various computational and biologically relevant metrics. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Protein-protein interaction networks Protein complexes Graph clustering method Greedy heuristic search algorithm Core proteins clustering model

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Boron Vacancies Causing Breakdown in 2D Layered Hexagonal Boron Nitride Dielectrics

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2019年第8期40卷 1321-1324页

作者： Ranjan, A. Raghavan, N. Puglisi, F. M. Mei, S. Padovani, A. Larcher, L. Shubhakar, K. Pavan, P. Bosman, M. Zhang, X. X. O'Shea, S. J. Pey, K. L. Singapore Univ Technol & Design Singapore 487372 Singapore ASTAR Inst Mat Res & Engn Singapore 138634 Singapore King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia Singapore Univ Technol & Design Dept Engn Prod Dev Singapore 487372 Singapore Univ Modena & Reggio Emilia DIEF I-42125 Modena Italy Appl Mat Inc Santa Clara CA 95054 USA Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore King Abdullah Univ Sci & Technol Phys Sci & Engn Div Thuwal 23955 Saudi Arabia

Dielectric breakdown in 2D insulating films for future logic device technology is not well understood yet, in contrast to the extensive insight we have in the breakdown of bulk dielectric films, such as HfO2 and SiO2. In this letter, we investigate the stochastic nature of breakdown (BD) in hexagonal boron nitride (h-BN) films using ramp voltage stress and examine the BD trends as a function of stress polarity, area, and temperature. We present evidence that points to a non-Weibull distribution for h-BN BD and use the multi-scale physics-based simulations to extract the energetics of the defects that are precursors to BD, which happens to be boron vacancies.

关键词： Boron vacancy clustering model dielectric breakdown hexagonal boron nitride ramp voltage stress

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Statistical nature of hard breakdown recovery in high-κ dielectric stacks studied using ramped voltage stress

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MICROELECTRONICS RELIABILITY 2018年 88-90卷 164-168页

作者： Feng, X. Raghavan, N. Mei, S. Dong, S. Pey, K. L. Wong, H. Zhejiang Univ Inst Microelect & Nanoelect Hangzhou 310027 Zhejiang Peoples R China Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

In replacing the conventional SiO2 gate dielectric with high-kappa materials, new challenges emerge on understanding the kinetics of dielectric breakdown due to the different properties of the new bulk oxide and the interfacial layers at the substrate and gate electrode interface as well. Among several complexities, dielectric relaxation and recovery have received a lot of attention due to their promising applications in resistive random access memory (RRAM). In this study, we explore the stochastic nature of hard breakdown recovery in HfO2, taking advantage of ramped voltage stress (RVS) measurements, which are theoretically equivalent to the widely used constant voltage stress (CVS), while being significantly less time-consuming. We found that the possibility of recovery is largely dependent on the ramp rate during RVS as the dielectric needs adequate time and sufficient thermal budget to recover. The clustering model is found to be a good fit to the RVS data sets for post-recovery subsequent breakdown events and the extent of defect clustering is found to be more intense after increasing number of recovery events. The breakdown mechanism in the stack is confirmed by measuring the resistance change trends with temperature.

关键词： clustering model Hard breakdown recovery High-kappa dielectric Ramped voltage stress Weibull distribution Weibull slope

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Role of metal nanocrystals on the breakdown statistics of flash memory high-κ stacks

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MICROELECTRONIC ENGINEERING 2017年 178卷 293-297页

作者： Feng, X. Raghavan, N. Mei, S. Du, L. Pey, K. L. Wong, H. Zhejiang Univ Inst Microelect & Nanoelect Hangzhou Zhejiang Peoples R China Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

We present a statistical perspective of the dielectric breakdown characteristics of flash memory devices constituting Au nanocrystallites (NCs) embedded in HfO2 successfully by using a defect generation and clustering model. The significant clustering and apparent high breakdown voltage in the NC sample are well-explained with the present of non-uniform spatial electric field patterns in the vicinity of the NCs. Simulation results for NC-embedded high-kappa dielectrics reveal some regions with suppressed and some with enhanced electric field in the dielectric film, with the field magnitude depending on the accumulated charge (Q(i)) in the NC and the size of NC. From the breakdown tests, a positive correlation between the Weibull slope (beta) and the ramp rate (RR) is observed, which is attributed to oxide thinning effect. In addition, temperature dependent current-voltage (I-V) characteristics clearly reveal that the change in E-field in the NC embedded stacks influences the voltage transition point for different conduction mechanisms to occur. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Breakdown clustering model Flash memory High-kappa dielectrics Metal nanocrystal Percolation

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Failure of Weibull distribution to represent switching statistics in OxRAM

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MICROELECTRONIC ENGINEERING 2017年 178卷 230-234页

作者： Raghavan, N. Singapore Univ Technol & Design Engn Prod Dev EPD Pillar 8 Somapah Rd Singapore 487372 Singapore

Considering the reasonable similarity in the physical mechanisms governing oxide breakdown and filamentary resistive switching, the use of the Weibull distribution has been widespread in the RRAM community for describing the stochastics of Forming, SET, RESET and read disturb data sets. Upon close examination, it is apparent that the Weibull fitting tends to be poor at the high percentiles for most of these data sets in RRAM (both vacancy based (OxRAM) and metal filament (CBRAM) based switching). The fundamental origin of these deviations is investigated in this work for OxRAM stacks through an analytical model making use of the percolation theory as the backbone framework and considering the physical process of thermochemical bond breaking that governs forming and SET events in resistance switching and the cycle-to-cycle variability inherent in the filament size and shape due to these atomistic events. Our model is compared with the standard Weibull distribution for fitting several data sets reported in the literature. The recently proposed defect clustering model is shown to be a better representative of the statistical physics governing the forming and SET switching process. (C) 2017 Elsevier B.V. All rights reserved.

关键词： clustering model Percolation Resistive switching Thermochemical model

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Asymmetric dielectric breakdown behavior in MgO based magnetic tunnel junctions

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MICROELECTRONIC ENGINEERING 2017年 178卷 308-312页

作者： Lim, J. H. Raghavan, N. Mei, S. Lee, K. H. Noh, S. M. Kwon, J. H. Quek, E. Pey, Ic. L. SUTD Engn Prod Dev Pillar Singapore 487372 Singapore GLOBALFOUNDRIES Singapore Pte Ltd Singapore 738406 Singapore

The time-dependent dielectric breakdown phenomenon (TDDB) has been investigated in a series of nominally identical MgO based magnetic tunnel junctions (MTJs) by pulsed voltage endurance test. Results from the pulsed endurance test reveal that the breakdown voltage is dependent on the polarity of the applied voltage. MTJs with "UP" current stress (I-up) (flowing from reference layer (RL) to free layer (FL)) show higher endurance than that of MTJs with "DOWN" current stress (I-down). We also found that bipolar stressing could result in reduced cumulative stress time before failure as compared to unipolar stressing. This could be explained by increased charge trapping/detrapping effects during bipolar stressing. The asymmetric breakdown behavior for different polarity was further supported by the different field acceleration slopes observed in the mean time to failure (MTTF) voltage bias trend line. Symmetric pulse scheme breakdown measurements were also carried out at different temperatures ranging from 25-85 degrees C. The time-dependent clustering model is applied here to best describe the breakdown statistics in view of the non-uniformity in percolation breakdown due to thickness variations (interface roughness) and other process-induced damages to the ultra-thin MgO barrier layer. (C) 2017 Elsevier B.V. All rights reserved.

关键词： clustering model Magnetic tunnel junctions MgO Pulsed voltage endurance Time-dependent dielectric breakdown

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A two-stage methodology for gene regulatory network extraction from time-course gene expression data

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EXPERT SYSTEMS WITH APPLICATIONS 2006年第1期30卷 59-63页

作者： Chan, ZSH Kasabov, N Collins, L Auckland Univ Technol KEDRI Auckland New Zealand Massey Univ Allan Wilson Ctr Mol Ecol & Evolut Palmerston North New Zealand

The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function;(2) gaining an understanding on the dynamic interaction between genes;(3) predicting gene expression values at future time points and accordingly;(4) predicting drug effect over time. In this paper, we propose a two-stage methodology that is implemented in the software 'Gene Network Explorer (GNetXP)' for extracting GRNs from gene trajectory data. In the first stage, we apply a hybrid Genetic Algorithm and Expectation Maximization algorithm on clustering the large number of gene trajectories using the mixture of multiple linear regression models for fitting the trajectory data. In the second stage, we apply the Kalman Filter to identify a set of first-order differential equations that describe the dynamics of the representative trajectories, and use these equations for discovering important gene interactions and predicting gene expression values at future time points. The proposed method is demonstrated on the human fibroblast response gene expression data. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： Gene Regulatory Network gene trajectory data clustering model

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TL-MOMFEA: a transfer learning-based multi-objective multitasking optimization evolutionary algorithm

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MEMETIC COMPUTING 2024年第3期16卷 387-402页

作者： Lu, Xuan Chen, Lei Liu, Hai-Lin Guangdong Univ Technol Sch Math & Appl Math Guangzhou 510520 Peoples R China

Evolutionary multi-objective multitasking optimization (MTO) has emerged as a popular research field in evolutionary computation. By simultaneously considering multiple objectives and tasks while identifying valuable knowledge for intertask transfer, MTO aims to discover solutions that deliver optimal performance across all objectives and tasks. Nevertheless, MTO presents a substantial challenge concerning the effective transport of high-quality information between tasks. To handle this challenge, this paper introduces a novel approach named TL-MOMFEA (multi-objective multifactorial evolutionary algorithm based on domain transfer learning) for MTO problems. TL-MOMFEA uses domain-transfer learning to adapt the population from one task to another, resulting in the reproduction of higher-quality solutions. Furthermore, TL-MOMFEA employs a model transfer strategy where population distribution rules learned from one task are succinctly summarized and applied to similar tasks. By capitalizing on the knowledge acquired from solved tasks, TL-MOMFEA effectively circumvents futile searches and accurately identifies global optimum predictions with increased precision. The effectiveness of TL-MOMFEA is evaluated through experimental studies in two widely used test suites, and experimental comparisons have shown that the proposed paradigm achieves excellent results in terms of solution quality and search efficiency, thus demonstrating its clear superiority over other state-of-the-art MTO frameworks.

关键词： Multitasking optimization Transfer learning Evolutionary algorithm clustering model

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Investigating energy balance and carbon footprint in saffron cultivation - a case study in Iran

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JOURNAL OF CLEANER PRODUCTION 2016年 115卷 162-171页

作者： Khanali, Majid Movahedi, Mehran Yousefi, Marziye Jahangiri, Sanaz Khoshnevisan, Benyamin Univ Tehran Fac Agr Engn & Technol Dept Agr Machinery Engn Karaj Iran Iranian Classificat Soc Dept Existing Vessels Tehran Iran

Saffron is one of the most expensive agricultural crops in the world which needs to be clearly monitored in the view of energy balance and carbon footprint in the production stage. Iran is the leading countries in saffron production and now accounts for approximately 60% of the world total production. As the biggest producer of saffron, analyzing the energy and carbon balance would help to produce this crop more economically and environmentally friendly. In the present study, the saffron cultivation in Southern Khorasan, a province in the east of Iran was taken into consideration and this cultivation was evaluated in terms of energy balance and carbon footprint. The evaluation was performed based on the data derived from whole life cycle of this crop i.e., a 6 year period. The assessment of energy balance demonstrated that on average, the total input energy consumed was 99,236 MJ ha(-1). More specifically, the average of energy indices i.e. energy ratio, energy productivity, and net energy were calculated 0.0044, 0.0003 kg MJ(-1), and -98,818.5 MJ ha(-1), respectively. The results clearly revealed that Nitrogen based fertilizers, farmyard manure and Phosphor based fertilizers dominated the total energy consumption meaning that their consumption management need to be given a priority. The total greenhouse gas emissions in saffron production was calculated on 10,897 kg CO2eq ha(-1) which showed a high degree of mismanagement in farm practices in the region meaning that without considerable reduction in saffron yield energy saving can be achieved. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： clustering model Energy analysis GHG emissions Saffron production

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