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BARTEL, ERIK, and ANDREAS SCHADSCHNEIDER. "QUANTUM CORNER — TRANSFER MATRIX DMRG." International Journal of Modern Physics C 19, no. 08 (August 2008): 1145–61. http://dx.doi.org/10.1142/s012918310801290x.
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We propose a new method for the calculation of thermodynamic properties of one-dimensional quantum systems by combining the TMRG approach with the corner transfer-matrix method. The corner transfer-matrix DMRG method brings reasonable advantage over TMRG for classical systems. We have modified the concept for the calculation of thermal properties of one-dimensional quantum systems. The novel QCTMRG algorithm is implemented and used to study two simple test cases, the classical Ising chain and the isotropic Heisenberg model. In a discussion, the advantages and challenges are illuminated.
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HALLBERG, KAREN. "RECENT APPLICATIONS OF THE DMRG METHOD." International Journal of Modern Physics B 20, no. 19 (July 30, 2006): 2624–35. http://dx.doi.org/10.1142/s0217979206035102.
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Since its inception, the DMRG method has been a very powerful tool for the calculation of physical properties of low-dimensional strongly correlated systems. It has been adapted to obtain dynamical properties and to consider finite temperature, time-dependent problems, bosonic degrees of freedom, the treatment of classical problems and non-equilibrium systems, among others. We will briefly review the method and then concentrate on its latest developments, describing some recent successful applications. In particular we will show how the dynamical DMRG can be used together with the Dynamical Mean Field Theory (DMFT) to solve the associated impurity problem in the infinite-dimensional Hubbard model. This method is used to obtain spectral properties of strongly correlated systems. With this algorithm, more complex problems having a larger number of degrees of freedom can be considered and finite-size effects can be minimized.
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Alvarez, Gonzalo. "Implementation of the SU(2) Hamiltonian symmetry for the DMRG algorithm." Computer Physics Communications 183, no. 10 (October 2012): 2226–32. http://dx.doi.org/10.1016/j.cpc.2012.04.025.
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Devakul, Trithep, Vedika Khemani, Frank Pollmann, David A. Huse, and S. L. Sondhi. "Obtaining highly excited eigenstates of the localized XX chain via DMRG-X." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2108 (October 30, 2017): 20160431. http://dx.doi.org/10.1098/rsta.2016.0431.
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We benchmark a variant of the recently introduced density matrix renormalization group (DMRG)-X algorithm against exact results for the localized random field XX chain. We find that the eigenstates obtained via DMRG-X exhibit a highly accurate l-bit description for system sizes much bigger than the direct, many-body, exact diagonalization in the spin variables is able to access. We take advantage of the underlying free fermion description of the XX model to accurately test the strengths and limitations of this algorithm for large system sizes. We discuss the theoretical constraints on the performance of the algorithm from the entanglement properties of the eigenstates, and its actual performance at different values of disorder. A small but significant improvement to the algorithm is also presented, which helps significantly with convergence. We find that, at high entanglement, DMRG-X shows a bias towards eigenstates with low entanglement, but can be improved with increased bond dimension. This result suggests that one must be careful when applying the algorithm for interacting many-body localized spin models near a transition. This article is part of the themed issue ‘Breakdown of ergodicity in quantum systems: from solids to synthetic matter’.
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Schollwöck, Ulrich. "The density-matrix renormalization group: a short introduction." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1946 (July 13, 2011): 2643–61. http://dx.doi.org/10.1098/rsta.2010.0382.
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The density-matrix renormalization group (DMRG) method has established itself over the last decade as the leading method for the simulation of the statics and dynamics of one-dimensional strongly correlated quantum lattice systems. The DMRG is a method that shares features of a renormalization group procedure (which here generates a flow in the space of reduced density operators) and of a variational method that operates on a highly interesting class of quantum states, so-called matrix product states (MPSs). The DMRG method is presented here entirely in the MPS language. While the DMRG generally fails in larger two-dimensional systems, the MPS picture suggests a straightforward generalization to higher dimensions in the framework of tensor network states. The resulting algorithms, however, suffer from difficulties absent in one dimension, apart from a much more unfavourable efficiency, such that their ultimate success remains far from clear at the moment.
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Oseledets, Ivan. "DMRG Approach to Fast Linear Algebra in the TT-Format." Computational Methods in Applied Mathematics 11, no. 3 (2011): 382–93. http://dx.doi.org/10.2478/cmam-2011-0021.
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AbstractIn this paper, the concept of the DMRG minimization scheme is extended to several important operations in the TT-format, like the matrix-by-vector product and the conversion from the canonical format to the TT-format. Fast algorithms are implemented and a stabilization scheme based on randomization is proposed. The comparison with the direct method is performed on a sequence of matrices and vectors coming as approximate solutions of linear systems in the TT-format. A generated example is provided to show that randomization is really needed in some cases. The matrices and vectors used are available from the author or at http://spring.inm.ras.ru/osel
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de Melo, Leonardo Alves Moreira, Marcus Vinícius Gonzaga Ferreira, and Flávio Henrique Teles Vieira. "Optimal Power Allocation and Delay Minimization Based on Conflict Graph Algorithm for Device-to-Device Communications." Applied Sciences 13, no. 24 (December 18, 2023): 13352. http://dx.doi.org/10.3390/app132413352.
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Device-to-device (D2D) technology is a promising technique in terms of being capable of providing efficiency, decreased latency, improved data rate, and increased capacity to cellular networks. Allocating power to users in order to reduce energy consumption and maintain quality of service (QoS) remains a major challenge in D2D communications. In this paper, we aim to maximize the throughput of D2D users and cellular users subject to QoS requirements and signal-to-interference-plus-noise ratio (SINR). To this end, we propose a resource and power allocation approach called optimal power allocation and delay minimization based on the conflict graph (OP-DMCG) algorithm that considers optimal power allocation for D2D multi-users in the cellular uplink channels and minimization of the total network delay using conflict graphs. Based on the simulations presented in this paper, we show that the proposed OP-DMCG algorithm outperforms the greedy throughput maximization plus (GTM+), delay minimization conflict graph (DMCG), and power and delay optimization based uplink resource allocation (PDO-URA) algorithms in terms of both total network throughput and total D2D throughput.
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Alekseeva, A. A., V. M. Bukharov, and V. M. Losev. "Diagnosis of hail based on DMRL-С and numerical modeling data." Hydrometeorological research and forecasting 2 (June 15, 2023): 114–27. http://dx.doi.org/10.37162/2618-9631-2023-2-114-127.
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The results of improving the algorithms for diagnosing hail on the Earth surface and its size based on the DMRL-C network and numerical prediction data are presented. The algorithms are implemented as part of an automated technology that operates in real time, with the presentation of results in a database and in the form of maps. A two-hour (relative to the observation period) animation of the diagnosed hail zones is provided. An algorithm for identifying the phase state of precipitation in a cloud is implemented, which made it possible to refine the diagnosis of hail in late spring and early autumn, rejecting cases with snow and ice pellets. The results of verifying the probability of detection of hail are given. It is concluded that the results of improving hail diagnosis using radar data will make it possible to refine data on cases of hail reaching the Earth surface in the European part of Russia, supplementing the already existing ones, according to the information of the network of meteorological and remote observations, which is also of practical importance for producing more accurate storm warnings about the occurrence of the phenomenon. Keywords: hail, diagnosis, phase state of precipitation in a cloud, radar data, DMRL-C network, automated technology
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SCHOLLWÖCK, ULRICH. "RECENT PROGRESS IN THE DENSITY-MATRIX RENORMALIZATION GROUP." International Journal of Modern Physics B 21, no. 13n14 (May 30, 2007): 2564–75. http://dx.doi.org/10.1142/s0217979207043890.
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Over the last decade, the density-matrix renormalization group (DMRG) has emerged as the most powerful method for the simulation of strongly correlated one-dimensional (1D) quantum systems. Input from quantum information has allowed to trace the method's performance to the entanglement properties of quantum states, revealing why it works so well in 1D and not so well in 2D; it has allowed to devise algorithms for time-dependent quantum systems and, by clarifying the link between DMRG and Wilson's numerical renormalization group (NRG), for quantum impurity systems.
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Zhang, Ai. "Influence of data mining technology in information analysis of human resource management on macroscopic economic management." PLOS ONE 16, no. 5 (May 18, 2021): e0251483. http://dx.doi.org/10.1371/journal.pone.0251483.
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The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.
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Alshamary, Haider Ali Jasim, Ahmad Sulaiman Abdullah, and Sadeq Adnan Hbeeb. "Nearest-neighbor field algorithm based on patchMatch for myocardial perfusion motion estimation/correction." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 843–50. http://dx.doi.org/10.11591/eei.v12i2.4216.
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Deformation correction and recovery of dynamic magnetic resonance images (DMRI) with low complexity algorithms without compromising image quality is a challenging problem. We proposed a motion estimation deformation-correction compressive sensing (DC-CS) scheme to recover dynamic images from its undersampled measurements. We simplify the complex optimization problem into three sub-problems. The contributions of this research are: introducing a global search strategy instead of the DC registration step, guaranteeing a non-explicit motion estimation that avoids any spatial alignment or registration of the images, and lowering the computational cost to the minimum by using PatchMatch (PM). The simulation result shows that the PM algorithm accelerates the recovery time without losing the quality in comparison with the DC algorithm.
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Dai, Guangyao, Chao Yang, Yingjie Liu, Tongbang Jiang, and Gervas Batister Mgaya. "A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis." Symmetry 11, no. 5 (May 22, 2019): 701. http://dx.doi.org/10.3390/sym11050701.
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Revealing brain functional connection pathways is of great significance in understanding the cognitive mechanism of the brain. In this paper, we present a novel rough set based dynamic multi-reduction algorithm (DMRA) to analyze brain functional connection pathways. First, a binary discernibility matrix is introduced to obtain a reduction, and a reduction equivalence theorem is proposed and proved to verify the feasibility of reduction algorithm. Based on this idea, we propose a dynamic single-reduction algorithm (DSRA) to obtain a seed reduction, in which two dynamical acceleration mechanisms are presented to reduce the size of the binary discernibility matrix dynamically. Then, the dynamic multi-reduction algorithm is proposed, and multi-reductions can be obtained by replacing the non-core attributes in seed reduction. Comparative performance experiments were carried out on the UCI datasets to illustrate the superiority of DMRA in execution time and classification accuracy. A memory cognitive experiment was designed and three brain functional connection pathways were successfully obtained from brain functional Magnetic Resonance Imaging (fMRI) by employing the proposed DMRA. The theoretical and empirical results both illustrate the potentials of DMRA for brain functional connection pathways analysis.
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Gao, Nan, Weiqi Shi, Xin Peng, Jing Huang, Cheng Xu, and Guoqi Xie. "Effective Real-Time Scheduling Optimization for Multi-Functional Mixed-Criticality Systems." Journal of Circuits, Systems and Computers 29, no. 14 (May 6, 2020): 2050226. http://dx.doi.org/10.1142/s0218126620502266.
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The heterogeneous distributed embedded systems integrated of multiple functions with different criticality levels are multi-functional mixed-criticality systems (MMCSs). The state-of-the-art work has studied the real-time scheduling in MMCS; however, it is not well designed in system switching mechanism and operation which may lead to missing the deadlines of high-criticality functions and redundant operation. In this study, we improve and optimize the problem by developing an algorithm called rearrangement-based scheduling for MMCS (RSM). The RSM algorithm optimizes the following two main aspects. The first aspect is optimizing system-criticality switching mechanisms, including system criticality changed up and down. The second aspect is the effective operation in system-criticality switching to reduce redundant operation. Experiments are performed, and results show that the RSM algorithm can achieve lower overall makespan and deadline miss ratios (DMRs) than the existing algorithms.
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Jeffcoate, William J. "Wound healing - a practical algorithm." Diabetes/Metabolism Research and Reviews 28 (January 23, 2012): 85–88. http://dx.doi.org/10.1002/dmrr.2235.
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Lee, Seungsoo, and George S. Dulikravich. "Distributed minimal residual (DMR) method for acceleration of iterative algorithms." Computer Methods in Applied Mechanics and Engineering 86, no. 2 (March 1991): 245–62. http://dx.doi.org/10.1016/0045-7825(91)90129-t.
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Komel, Neža Ema, Klemen Kozmus Trajkovski, and Dušan Petrovič. "Upgrade of existing algorithms for creating contour lines on topographic maps of the karst surface." Abstracts of the ICA 2 (October 8, 2020): 1–2. http://dx.doi.org/10.5194/ica-abs-2-19-2020.
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Abstract. Today, many software tools enable the production of contour lines from relief models, but the results of modelling complex karst relief are often inadequate. Reasons for this may be limited quality and resolution of relief models, limitations of algorithms for calculating contours, or limitations of algorithms for smoothing and displaying additional symbols that further describe relief, such as slope lines, steep slopes and smaller objects that cannot be effectively displayed with contours, etc.We will present research in the field of improving existing algorithms in rugged karst terrain. As a target result, the presentation of relief on the existing national topographic maps in Slovenia, which were made by manual photogrammetric survey of aerial photos stereo pairs, were used. Slovenian elevation model DMR1 (1 m density) is used as a source for the creation of contour lines in various commercial software packages, and by comparing the results with a relief presentation on a topographic map, we selected the most appropriate basic algorithm. This one is further upgraded mainly by enabling automatic selection of auxiliary contour lines in the area, presentation of individual smaller relief objects with appropriate point or linear symbols, addition of slope lines on contours and indications in the middle of depressions and displacement of contour lines in order to better depict the terrain morphology.The results were tested in four different areas in Slovenia. Figure 1 shows the contour lines for a testing area near village Opatje Selo near Slovenia-Italy border, which were made by the best commercial software. The results of the algorithm are shown in Figure 2. The comparison between the results of the algorithm and the national topographic maps in the chosen scale gave promising results. In future work, we are planning to extend the algorithm so that it will be able to provide modelling of different terrains in the region.
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Misbahuddin, Misbahuddin, Anak Agung Putri Ratna, and Riri Fitri Sari. "Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs." International Journal of Computers Communications & Control 13, no. 1 (February 12, 2018): 99. http://dx.doi.org/10.15837/ijccc.2018.1.3088.
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In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.
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Alekseeva, A. A., V. M. Bukharov, E. V. Vasil’ev, and V. M. Losev. "Diagnosis of squalls in snowstorms based on DMRL-C Doppler weather radar data." Hydrometeorological research and forecasting 3 (October 16, 2020): 6–18. http://dx.doi.org/10.37162/2618-9631-2020-3-6-18.
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The results of studying the diagnosis of squalls in snowstorms based on DMRL-C Doppler weather radar data are presented. The diagnosis of squalls is performed using the algorithm developed in the Hydrometcentre of Russia for diagnosing showers based on the maximum convective velocity calculated from radar data (maximum radar reflectivity in a cloud and the cloud top height). The algorithm implies the determination of the fact of the occurrence of squalls for three speed gradations (15-19 m/s; 20-24 m/s; ≥25 m/s) and the refinement of wind speed during a squall. Keywords: diagnosis, radar data, snowstorm, squall Fig. 6. Ref. 7.
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WANG Yan. "Research on DMR QoS Optimization Algorithm Based on Ad-Hoc Network." International Journal of Advancements in Computing Technology 5, no. 2 (January 31, 2013): 258–64. http://dx.doi.org/10.4156/ijact.vol5.issue2.34.
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Luo, Zhen, Yingjin Ma, Chungen Liu, and Haibo Ma. "Efficient Reconstruction of CAS-CI-Type Wave Functions for a DMRG State Using Quantum Information Theory and a Genetic Algorithm." Journal of Chemical Theory and Computation 13, no. 10 (September 27, 2017): 4699–710. http://dx.doi.org/10.1021/acs.jctc.7b00439.
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Kreutz, Clemens, Nilay S. Can, Ralf Schulze Bruening, Rabea Meyberg, Zsuzsanna Mérai, Noe Fernandez-Pozo, and Stefan A. Rensing. "A blind and independent benchmark study for detecting differentially methylated regions in plants." Bioinformatics 36, no. 11 (March 17, 2020): 3314–21. http://dx.doi.org/10.1093/bioinformatics/btaa191.
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Abstract Motivation Bisulfite sequencing (BS-seq) is a state-of-the-art technique for investigating methylation of the DNA to gain insights into the epigenetic regulation. Several algorithms have been published for identification of differentially methylated regions (DMRs). However, the performances of the individual methods remain unclear and it is difficult to optimally select an algorithm in application settings. Results We analyzed BS-seq data from four plants covering three taxonomic groups. We first characterized the data using multiple summary statistics describing methylation levels, coverage, noise, as well as frequencies, magnitudes and lengths of methylated regions. Then, simulated datasets with most similar characteristics to real experimental data were created. Seven different algorithms (metilene, methylKit, MOABS, DMRcate, Defiant, BSmooth, MethylSig) for DMR identification were applied and their performances were assessed. A blind and independent study design was chosen to reduce bias and to derive practical method selection guidelines. Overall, metilene had superior performance in most settings. Data attributes, such as coverage and spread of the DMR lengths, were found to be useful for selecting the best method for DMR detection. A decision tree to select the optimal approach based on these data attributes is provided. The presented procedure might serve as a general strategy for deriving algorithm selection rules tailored to demands in specific application settings. Availability and implementation Scripts that were used for the analyses and that can be used for prediction of the optimal algorithm are provided at https://github.com/kreutz-lab/DMR-DecisionTree. Simulated and experimental data are available at https://doi.org/10.6084/m9.figshare.11619045. Contact ckreutz@imbi.uni-freiburg.de Supplementary information Supplementary data are available at Bioinformatics online.
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Shen, Shu Yi, Iulia Cirlan, Felicia Vincelli, Ben Brown, Jun Min, Justin Burgener, Junjun Zhang, et al. "Abstract 5024: Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA." Cancer Research 84, no. 6_Supplement (March 22, 2024): 5024. http://dx.doi.org/10.1158/1538-7445.am2024-5024.
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Abstract Introduction: Plasma-derived cell-free DNA (cfDNA) can be used to identify cancer signals, including minimal residual disease (MRD), in patients who have undergone curative cancer treatments. The cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) methodology is combined with custom algorithms that leverage differentially methylated regions (DMRs) found in cfDNA to distinguish between cancer and non-cancer signals. This novel non-degradative, tissue-agnostic approach was developed to bypass the limitations of bisulfite-sequencing and tissue-informed methods used in other liquid biopsy technologies. Here we present preliminary analytical performance metrics of an algorithm in development for detecting MRD. Methods: In the genome-wide methylome enrichment platform, plasma-derived cfDNA is subjected to standard library preparation, combined with DNA filler, denatured, and immunoprecipitated using an anti-5-mC antibody. Captured DNA is amplified and sequenced. For this study, a candidate algorithm comprised of differentially methylated regions (DMRs) and 12 cancer types was used to quantify cancer-specific methylation. Control samples from 12 non-cancer donors were used to establish a selected 95% true negative rate threshold. This threshold was used to determine assay sensitivity, which was evaluated using contrived cancer samples intended to mimic low-level circulating tumor DNA (ctDNA) representative of MRD. Samples were contrived by titrating fragmented DNA from three immortalized tumor-derived cell-lines into plasma-derived cfDNA in a titration series targeting <1% ctDNA levels. Cell-lines were of non-small cell lung cancer and head and neck squamous cell carcinoma origin; titration series were replicated for a total of 65 tests. Results: All non-cancer and contrived cancer samples met in-process quality control metrics. This included ≥98.5% methylation specificity and ≥80 million unique molecules. Limit of detection calculations at 95% sensitivity (LoD95) were <0.1%. Conclusions: Tissue-agnostic approaches for detecting cancer signals from plasma have significant benefit, especially in settings where tissue is not accessible for evaluation. However, these tests must have highly sensitive methods of cancer signal detection for clinical applications. Our preliminary analytical data demonstrates the use of a blood-based, tissue- agnostic genome-wide methylome enrichment platform utilizing non-degradative methodology combined with specific algorithms and DMRs for MRD quantification and prognostic prediction, using treatment-naïve plasma samples. Future studies in post-treatment and longitudinal samples are ongoing to evaluate the utility of this genome-wide methylome enrichment platform for cancer management. Citation Format: Shu Yi Shen, Iulia Cirlan, Felicia Vincelli, Ben Brown, Jun Min, Justin Burgener, Junjun Zhang, Yulia Newton, Margaret Gruca, Abel Licon, Jing Zhang, Anne-Renee Hartman, Alan Williams, Hestia Mellert, Daniel D. De Carvalho. Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5024.
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Zhang, Jialu, and Xiaotong Zhang. "Diffusion-Weighted Imaging of the Macaque Brain Using Diffusion-Prepared Turbo Spin Echo in MRI." Applied Sciences 11, no. 24 (December 18, 2021): 12077. http://dx.doi.org/10.3390/app112412077.
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Magnetic resonance imaging (MRI) integrates a static magnetic field, a time-varying gradient magnetic field at kHz and a radio-frequency (RF) magnetic field for non-invasive and real-time imaging; meanwhile, diffusion MRI (dMRI) pushes a further and closer dimension to the scale of neural fibers through sensitizing the gradient field to recognize water molecular displacement over distances of 1~20 μm along fibers. Contemporary dMRI approaches face challenges of magnetic field inhomogeneity as well as sequence-associated distortion and signal loss, the common remedies of which are repeated scans and post-reconstruction algorithms. In this study, over an anesthetized macaque with a customized head coil on 3 T MRI, we have proposed and implemented a monopolar diffusion-prepared module for turbo spin echo sequence (DP-TSE) as an alternative to achieve distortion-free, high-resolution diffusion imaging with improved SNR. The results showed high image quality and SNR efficiency as compared with conventional dMRI methods at millimeter level, allowing us to pursue submillimeter-scale dMRI over non-human primates (NHPs) in a relatively short scan time and without repetitions or post-processing, which could merit and advance our understanding of the structure and organizations of the primate’s brain.
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Lorier Roy, Élisabeth. "Nævus de Spitz : revue des aspects dermoscopiques et algorithme de prise en charge." Dermato Mag 11, S4 (September 1, 2023): 1–5. http://dx.doi.org/10.1684/dmg.2023.622.
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Suleymanoglu, Baris, and Metin Soycan. "Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey." Geodetski vestnik 63, no. 03 (2019): 395–414. http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.395-414.
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Hu, Shuo, Jinsheng Tao, Minhua Peng, Bo Wang, Zhujia Ye, Zhiwei Chen, Haisheng Chen, Haifeng Yu, Jianbing Fan, and Bin Ni. "Early detection of lung cancer using a panel of circulating cell-free DNA methylation biomarkers." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e20614-e20614. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e20614.
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e20614 Background: Lung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers. Methods: This prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease from The First Affiliated Hospital of Soochow University between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance (mRMR) algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples. Results: We identified 7 DMRs corresponding to 7 differentially methylated genes (DMRs, including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1), highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissues. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.966 (95%CI: 0.933-1.000)/0.961 (0.924-1.000), sensitivities of 0.887 (0.824-0.951)/0.922 (0.863-0.980), specificities of 0.938 (0.889-0.986)/1.000 (1.000-1.000), and accuracies of 0.896 (0.835-0.957)/0.938 (0.886-0.991) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.935 (0.862-1.000), sensitivity of 0.808 (0.733-0.883), specificity of 0.975 (0.945-1.000), and accuracy of 0.934 (0.887-0.981). Conclusions: The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.
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Alvarez-Guisasola, Fernando, Ana M. Cebrián-Cuenca, Xavier Cos, Manuel Ruiz-Quintero, Jose M. Millaruelo, Avivit Cahn, Itamar Raz, and Domingo Orozco-Beltrán. "Calculating individualized glycaemic targets using an algorithm based on expert worldwide diabetologists: Implications in real-life clinical practice." Diabetes/Metabolism Research and Reviews 34, no. 3 (January 24, 2018): e2976. http://dx.doi.org/10.1002/dmrr.2976.
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Hasbún, Rodrigo, Carolina Iturra, Soraya Bravo, Boris Rebolledo-Jaramillo, and Luis Valledor. "Differential Methylation of Genomic Regions Associated with Heteroblasty Detected by M&M Algorithm in the Nonmodel SpeciesEucalyptus globulusLabill." International Journal of Genomics 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/4395153.
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Epigenetic regulation plays important biological roles in plants, including timing of flowering and endosperm development. Little is known about the mechanisms controlling heterochrony (the change in the timing or rate of developmental events during ontogeny) inEucalyptus globulus. DNA methylation has been proposed as a potential heterochrony regulatory mechanism in model species, but its role during the vegetative phase inE. globulushas not been explored. In order to investigate the molecular mechanisms governing heterochrony inE. globulus, we have developed a workflow aimed at generating high-resolution hypermethylome and hypomethylome maps that have been tested in two stages of vegetative growth phase: juvenile (6-month leaves) and adult (30-month leaves). We used the M&M algorithm, a computational approach that integrates MeDIP-seq and MRE-seq data, to identify differentially methylated regions (DMRs). Thousands of DMRs between juvenile and adult leaves ofE. globuluswere found. Although further investigations are required to define the loci associated with heterochrony/heteroblasty that are regulated by DNA methylation, these results suggest that locus-specific methylation could be major regulators of vegetative phase change. This information can support future conservation programs, for example, selecting the best methylomes for a determinate environment in a restoration project.
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Pesce, Marica, Audrey Repetti, Anna Auría, Alessandro Daducci, Jean-Philippe Thiran, and Yves Wiaux. "Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors." Journal of Imaging 7, no. 11 (October 27, 2021): 226. http://dx.doi.org/10.3390/jimaging7110226.
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High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
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Zampini, Matteo, Claudia Tregnago, Valeria Bisio, Benedetta Accordi, Valentina Serafin, Paolo Pierani, Nicola Santoro, et al. "Dna Methylation Is Linked to a Specific Cell-Adhesion Program in Relapsed Pediatric t(8;21)(q22;q22)RUNX1-RUNX1T1 Patients." Blood 128, no. 22 (December 2, 2016): 1524. http://dx.doi.org/10.1182/blood.v128.22.1524.1524.
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Abstract t(8;21)(q22;q22)RUNX1-RUNX1T1 is a recurrent somatic lesion detected at diagnosis in approximately 12-15% of children with acute myeloid leukemia (AML). Children with this isolated translocation are usually considered at standard risk, but our last multicenter trial revealed a higher than expected cumulative incidence of relapse for these patients1. Genetic and epigenetic heterogeneity is emerging as a fundamental property of AML in the context of the clonal architecture dynamic evolution. In view of this observation, we hypothesized that within t(8;21) patients there may coexist a complex mosaic of cells containing combinations of the same genetic t(8,21) lesion together with different epigenetic variants, and that epigenetic complexity may play a crucial role in predisposing patients to relapse. The importance of the identification of molecular markers distinctive of t(8,21)-rearranged patients prone to develop relapse could be instrumental to improve their cure rate. We performed high throughput DNA methylation profiling (RRBS-seq) and integrated results with gene expression profiling (Affymetrix HTA 2.0) of 16 isolated t(8;21) AML samples collected at diagnosis, and analyzed data by comparing patients who did or did not experience relapse. We applied a logistic regression algorithm to identify differentially methylated regions (DMRs) considering a minimum change in methylation level of 25%. We validated results in a proteome context by reverse phase protein array (RPPA) in an independent cohort of 35 t(8;21) AML patients. DNA methylation profiling analysis identified 337 DMRs able to correctly predict t(8;21) patients who did relapse from those who did not. In particular, 23 DMRs (7%) were located at promoters, while most of them were equally distributed between intronic (48%) and exonic (45%) regions. Globally, we found hypomethylated DMRs being significantly enriched in relapsed patients, in particular in repetitive elements regions of the genome (LINE, SINE, DNA transposon: 38.9% vs 52.4%; p<0.01), supporting an enhanced transposable elements transcription contributing to cancer genomic instability. DMRs clustering analysis correctly divided t(8,21) patients according to their risk of experiencing relapse, independently of their different localization (at promoters, exons or introns), revealing that DNA methylation profiling has a predictive role for identifying patients with worse event-free survival. We then considered the role of methylation over gene expression and found a weak correlation between DMRs (mostly at promoters) and their associated gene levels (14.5% of DMRs with an inverse correlation r <-0.4, p<0.05). To better understand the role of DMRs and transcriptional regulation, we searched for associations between DMRs and chromatin modification patterns. DMRs were enriched at regulatory regions; in particular, we found hypermethylation in promoter and enhancer regions, while hypomethylation was found in repressed chromatin regions (p<0.05). Looking at the transcription factors (TFs) binding sites within the DMRs, we identified that at hypermetylated DMRs the most represented TFs were E2F1 and HDAC1, suggesting they might be almost transcriptional silenced. By contrast, MAFK and FOXA2 binding sites were enriched at hypomethylated sites, suggesting their enhanced activity in relapsed patients as compared to the non-relapsed ones. Finally, we interrogated gene ontology for DMRs-associated genes and deregulated genes found by GEP, showing a significant enrichment for pathways involved in cell adhesion and cytoskeletal organization. Proteome analysis by RPPA validated these pathways being aberrant activated (global test p<0.01) in an independent cohort of t(8;21)-rearranged patients, and supported the ongoing in vitro experiments in t(8;21) cell lines to define candidates genes involved in the pathophysiology of t(8,21) relapse. These data show that the methylation signature may be considered a novel, emerging diagnostic tool making possible to better stratifying t(8,21)-rearranged patients through the identification, already at diagnosis, of those who are prone to relapse . Preliminary data of functional analysis suggest that epigenome of t(8;21) blasts may control cell adhesion properties at bone marrow niche and treatment response, contributing to patients relapse. 1 Pession A, Blood. 2013;122(2):170-8. Disclosures No relevant conflicts of interest to declare.
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Carnevale, Vincenzo, Susanna Morano, Andrea Fontana, Maria Antonietta Annese, Mara Fallarino, Tiziana Filardi, Massimiliano Copetti, et al. "Assessment of fracture risk by the FRAX algorithm in men and women with and without type 2 diabetes mellitus: a cross-sectional study." Diabetes/Metabolism Research and Reviews 30, no. 4 (April 7, 2014): 313–22. http://dx.doi.org/10.1002/dmrr.2497.
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Jallais, Maëliss, Pedro L. C. Rodrigues, Alexandre Gramfort, and Demian Wassermann. "Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements." Machine Learning for Biomedical Imaging 1, IPMI 2021 (May 8, 2022): 1–28. http://dx.doi.org/10.59275/j.melba.2022-a964.
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Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model. To address this issue, we propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model. As opposed to other approaches from the literature, our algorithm yields not only an estimation of the parameter vector θ that best describes a given observed data point x0 , but also a full posterior distribution p(θ|x0) over the parameter space. This enables a richer description of the model inversion, providing indicators such as credible intervals for the estimated parameters and a complete characterization of the parameter regions where the model may present indeterminacies. We approximate the posterior distribution using deep neural density estimators, known as normalizing flows, and fit them using a set of repeated simulations from the forward model. We validate our approach on simulations using dmipy and then apply the whole pipeline on two publicly available datasets.
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Chen, Junbo, Shouyin Liu, and Min Huang. "Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction." Journal of Healthcare Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9856058.
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The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods.
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Borah, Firoz Kanti, Leung Tsang, and Edward J. Kim. "SWE Retrieval Algorithms Based on the Parameterized Bi-continuous DMRT Model without Priors on Grain Size or Scattering Albedo." Progress In Electromagnetics Research 178 (2023): 129–47. http://dx.doi.org/10.2528/pier23071101.
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Magueta, Roberto, João Domingues, Adão Silva, and Paulo Marques. "Effective PSCCH Searching for 5G-NR V2X Sidelink Communications." Electronics 10, no. 22 (November 17, 2021): 2827. http://dx.doi.org/10.3390/electronics10222827.
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Cooperative Intelligent Transport Systems (C-ITS) are essential for increasing road safety and to make road transport more efficient, sustainable, and environmentally friendly. The implementation of C-ITS technology is only possible through the connectivity of Vehicle-to-Everything (V2X), which allows the interconnection of vehicles in a network and with road support infrastructure. However, real-time systems require efficient signal processing in order to respond within the necessary time. Some of this processing is related to searching the Physical Sidelink Control Channel (PSCCH), where a blind algorithm is commonly used. However, this algorithm is quite inefficient to searching the PSCCH, since all the processing should be completed several times before successful decoding it. Therefore, the aim of this paper is to design a more efficient algorithm to search/decode the PSCCH. In the proposed algorithm, we firstly compute all the correlations between the received signal and the Demodulation Reference Signal (DMRS), and the remaining conventional processing to decode the PSCCH is only performed over the subchannels with higher correlation, which leads to a strong complexity reduction. The proposed algorithm is evaluated and compared with the conventional blind algorithm. The results have shown a significant performance improvement in terms of runtime.
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Meldi, Kristen, Tingting Qin, Francesca Buchi, Jean-Baptiste Micol, Jason Sotzen, Erico Masala, Bernadino Allione, et al. "Distinct DNA Methylation and Expression Profiles Underlie CMML Responsiveness to Decitabine and Uncover Novel Mechanism of Resistance." Blood 124, no. 21 (December 6, 2014): 4598. http://dx.doi.org/10.1182/blood.v124.21.4598.4598.
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Abstract Myelodysplastic syndromes (MDS) and the related disorder chronic myelomonocytic leukemia (CMML) are characterized by abnormal DNA hypermethylation and the DNA methyltransferase inhibitors (DMTis) azacytidine and decitabine (DAC) are frequently used as frontline therapy in these patients. However, DMTis are ineffective for ~50% of the patients who must still undergo treatment for at least 6 months before they can be deemed resistant. Therefore, it is of critical importance to identify baseline molecular differences associated with DMTi sensitivity that can help (i) to improve patient risk-stratification at diagnosis and (ii) identify the underlying mechanisms of resistance to these agents. Previous efforts to identify baseline DNA methylation differences at promoter regions between DMTi responders and non-responders have not been successful, so we hypothesized that any potential differences would be located distally from promoter regions. For this purpose we studied 40 CMML patients at diagnosis, all of whom had been uniformly treated with DAC 20 mg/m2/day x 5 days as frontline therapy. After 6 cycles of therapy patients were classified as responders (n=19, hematological improvement or better), or non-responders (n=21, stable or progressive disease). Mutational analysis showed no significant differences in the frequency of mutations in TET2, ASXL1, DNMT3A, RUNX1, TP53, JAK2, KIT, KRAS, EZH2, IDH1/2 and spliceosome genes. Using Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) we analyzed the baseline methylation status at ~3M CpG sites across the genome of 39/40 CMML patients. We identified 158 statistically significant differentially methylated regions (DMRs) (FDR<0.1 and methylation difference ≥25%) between the two groups. DAC-sensitive patients displayed both regions of higher methylation as well as regions with lower methylation compared to DAC-resistant patients. As predicted, DMRs were depleted at promoters (DMRs 9% vs. Background [BG] 21%, p-value: 3.4×10-5) and CpG islands (DMRs 8% vs. BG 25%, p-value: 1.5×10-8). Further analysis showed that hypermethylated DMRs were enriched at intronic regions (Hyper DMRs 58% vs. BG 33%, p-value: 3.7×10-6) while hypomethylated DMRs were enriched at intergenic regions (Hypo DMRs 49% vs. BG 38%, p-value: 2.6×10-2). Moreover, hypermethylated DMRs were significantly enriched for enhancer regions, and in particular, enhancers located within gene bodies (hyper DMRs 38% vs. BG 18%, p-value: 2.3×10-5). KEGG pathway analysis showed a significant enrichment of DMRs in the MAPK signaling pathway (FDR<0.01). Next, using a support vector machine algorithm with 10-fold cross validation we were able to develop a classifier capable of predicting response to DAC with high level of accuracy (ROC AUC: 0.99) based solely on the DNA methylation status at diagnosis of 17 genomic regions. Three different random splits of the cohort into training and test sets achieved correct predictions for 85.7%, 89.47%, and 100% of cases, respectively, demonstrating the accuracy and potential utility of such a classifier. Finally, RNA-seq analysis identified 53 differentially expressed genes between responders (n=8) and non-responders (n=6) at diagnosis. Genes implicated in cell cycle and DNA replication were overexpressed in responders. By contrast, very few genes were overexpressed at the time of diagnosis in primary resistant patients. Among these were CXCL4 and CXCL7 which, given their reported contributions to cell cycle arrest and chemoresistance, were tested for their functional roles in DAC resistance. Pre-treatment of normal CD34+ cells for 72 h with 10nM DAC significantly reduced colony formation (p<0.05) but the addition of 50ng/mL of CXCL4 and CXCL7 restored colony formation to that of untreated cells. Moreover, treatment of primary CMML cells with 10 nM DAC for 72h significantly reduced viability of these cells, while concomitant treatment with 50ng/mL of CXCL4 and CXCL7 was sufficient to abrogate this effect. Taken together, our findings demonstrate that (i) specific DNA methylation profiles targeting non-promoter regulatory regions are associated with DAC sensitivity, (ii) these differences can be harnessed for the development of clinical biomarkers predictive of response and (iii) we identified a novel mechanism of resistance to DAC mediated through two chemokines that are exclusively overexpressed in non-responders. Disclosures No relevant conflicts of interest to declare.
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Conconi, Michele, Filippo De Carli, Matteo Berni, Nicola Sancisi, Vincenzo Parenti-Castelli, and Giuseppe Monetti. "In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI." Applied Sciences 13, no. 1 (January 3, 2023): 629. http://dx.doi.org/10.3390/app13010629.
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The in-vivo quantification of knee motion in physiological loading conditions is paramount for the understanding of the joint’s natural behavior and the comprehension of articular disorders. Dynamic MRI (DMRI) represents an emerging technology that makes it possible to investigate the functional interaction among all the joint tissues without risks for the patient. However, traditional MRI scanners normally offer a reduced space of motion, and complex apparatus are needed to load the articulation, due to the horizontal orientation of the scanning bed. In this study, we present an experimental and computational procedure that combines an open, weight-bearing MRI scanner with an original registration algorithm to reconstruct the three-dimensional kinematics of the knee from DMRI, thus allowing the investigation of knee deep-flexion under physiological loads in space. To improve the accuracy of the procedure, an MR-compatible rig has been developed to guide the knee flexion of the patient. We tested the procedure on three volunteers. The overall rotational and positional accuracy achieved are 1.8° ± 1.4 and 1.2 mm ± 0.8, respectively, and they are sufficient for the characterization of the joint behavior under load.
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Melton, Collin A., Peter Freese, Yifan Zhou, Archana Shenoy, Siddhartha Bagaria, Christopher Chang, Chih-Chung Kuo, et al. "A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns." Cancers 16, no. 1 (December 23, 2023): 82. http://dx.doi.org/10.3390/cancers16010082.
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Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman’s correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10−3 and 10−4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.
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Wang, Xingqiang, Dongyun Lei, Jie Ding, Shuang Liu, Li Tao, Fan Zhang, Jiangyun Peng, and Jian Xu. "A DNA-Methylated Sight on Autoimmune Inflammation Network across RA, pSS, and SLE." Journal of Immunology Research 2018 (August 12, 2018): 1–13. http://dx.doi.org/10.1155/2018/4390789.
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Methylation variabilities of inflammatory cytokines play important roles in the development of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and primary Sjögren’s syndrome (pSS). With heightened focus on personalized and precise medicine, it is necessary to compare and contrast the difference and similarity of cytokine methylation status between the 3 most classic autoimmune diseases (AIDs). In this study, we integrated 5 Cytokine-Chips from genome-wide DNA methylation datasets of the 3 kind of AIDs, delta-beta value was calculated for intergroup difference, and comprehensive bioinformatics analyses of cytokine genes with aberrant methylations were performed. 125 shared differential methylation variabilities (DMVs) were identified. There were 102 shared DMVs with similar methylation status; 3 hypomethylated differential methylation regions (DMRs) across the AIDs were found, and all 3 DMRs were hypomethylated. DMRs (AZU1, LTBR, and RTEL1) were likely to serve as activator in the inflammatory process. Particularly, AZU1 and LTBR with hypomethylated TSS and first exon located in the promoter regions were able to trigger inflammation signaling cascades and play critical roles in autoimmune tautology. Moreover, functional epigenetic module (FEM) algorithm showed that different inflammatory networks are involved in different AIDs; 5 hotspots were identified as biologically plausible pathways in inducing or perpetuating of inflammation which are epigenetically deregulated in AIDs. We concluded methylation variabilities among the same cytokines can greatly impact the perpetuation of inflammatory process or signal pathway of AIDs. Differentiating the cytokine methylation status will serve as valuable resource for researchers alike to gain better understanding of the epigenetic mechanisms of the three AIDs. Even more importantly, better understanding of cytokine methylation variability existing between the three classic AIDs will aid in identification of potential epigenetic biomarkers and therapeutic targets. This trial is registered with ChiCTR-INR-16010290, a clinical trial for the treatment of rheumatoid arthritis with Warming yang and Smoothening Meridians.
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Xiangyang, Zhu, Dai Hua, Yi Xun, Yang Geng, and Li Xiao. "MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data." Security and Communication Networks 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/1923476.
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With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.
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Shen, Wenhao, Hang Dong, Haoran Tang, Yue Zhang, Shidong Jia, and Yang Luo. "Diagnosis of prostate cancer using cell-free DNA methylation profiles from expressed prostatic secretions." Journal of Clinical Oncology 41, no. 6_suppl (February 20, 2023): 389. http://dx.doi.org/10.1200/jco.2023.41.6_suppl.389.
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389 Background: Urinary and seminal cell-free DNA (cfDNA) have been recognized as promising biomarkers of prostate cancer. DNA methylation signal in tumor is increasingly used as tumor diagnosis and longitudinal monitoring indicator. However, the clinical utility of cfDNA from expressed prostatic secretions (EPS) remains unknown. Methods: The prospective study includes 50 prostate cancer (PC) patients and equivalent benign prostatic hyperplasia (BPH) patients, where EPS samples were collected after the prostatic massage of each patient. Cell-free DNA from EPS was extracted and treated by PredicineEPIC, a liquid biopsy comprehensive DNA methylation assay. The study developed integrated bioinformatic algorithms to profile the genome-wide epigenomic characteristics and investigate tumor-specific methylation patterns. Results: The initial exploratory cohort included 5 PC and 11 BPH cases. PredicineEPIC whole-genome DNA methylation of EPS identified over 300 differentially methylated regions (DMRs). Prostate cancers were distinguished from the BPH group by an unsupervised clustering method, suggesting that the identified DMRs embody the specific profiles of malignant tumors. Additionally, this study detected genome-wide copy number variation burden (CNB) in parallel. A risk model was built for cancer risk assessment based on methylation and CNB characteristics. Conclusions: This study demonstrated the feasibility of methylation profiling of cfDNA in EPS in prostate cancer. This non-invasive liquid biopsy approach could sensitively navigate prostate cancer from benign prostatic hyperplasia, suggesting future direction of methylation-based liquid biopsy in detecting prostate cancer.
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Kinross, James M., Pol Canal-Noguer, Marko Chersicola, Primož Knap, Marko Bitenc, Alexandre Perera-Lluna, Michael H. A. Roehrl, and Kristi Kruusmaa. "Accurate early-stage colorectal cancer detection through analysis of cell-free circulating tumor DNA (ctDNA) methylation patterns." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 3606. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.3606.
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3606 Background: Colorectal cancer (CRC) screening programs suffer from poor uptake and biomarkers have limited diagnostic accuracy. The measurement of the methylation status of tumor-derived cell-free DNA in plasma may address these challenges. We used a targeted methylation panel, tumor-derived signal deduction and machine learning algorithm to refine a blood test for the detection of early-stage CRC. Methods: This was a prospective, international multicenter observational cohort study. Plasma samples were collected either prior to a scheduled colonoscopy as part of standard colorectal cancer screening or prior to colonic surgery for primary CRC. Differentially methylated regions (DMRs) were initially selected by analyzing CRC and control tissue samples with whole genome bisulfite sequencing. A targeted sequencing assay was designed to capture these DMRs in plasma ctDNA. Individual sequencing reads were evaluated for cancer-specific methylation signal and scores calculated for each DMR in a sample. A panel of methylation scores originating from 203 DMRs was used in a prediction model building and validated in a test cohort of patients. Results: Calculated scores were used to train a machine learning model on 68 ctDNA samples from 18 early stage (I-II) and 16 late-stage (III-IV) CRC patients and 34 age, BMI, gender and country of origin matched neoplasia-free controls (median age 63 [50-74], mean BMI 27 [19.5-37], female 50%, Spanish and Ukrainian population, distal cancers 50%). This model was then applied to an independent set of subjects from Spanish, Ukraine and Germany, including 36 stage I-IV cancer patients (median age 61.5 [55-82], BMI 28 [16-39], female 47%, 42% of the tumors were distal) and 159 age and sex matched controls. 87 of the control patients had a negative colonoscopy finding (cNEG), 19 had hyperplastic polyps (HP), 37 had small non-advanced adenomas (NAA) and 16 were diagnosed with other benign gastrointestinal diseases (GID). The model correctly classified 92% (33/36) of CRC patients. Sensitivity per cancer stage ranged from 83% (5/6) for stage I, 92% (11/12) for stage II, 92% (12/13) for stage III to 100% (5/5) for stage IV. Specificity of the model was 97% (154/159), with 100% (37/37) NAA, 94% (15/16) GID, 95% (18/19) HP and 97% cNEG patients correctly identified. Lesion location, gender, BMI, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Methylation sequencing data analyzed using read-wise scoring approach combined with a machine-learning algorithm is highly diagnostic for early-stage (I-II) CRCs (89% sensitivity at 97% specificity). This method could serve as the basis for a highly accurate and minimally invasive blood-based CRC screening test with significant implications for the clinical utility of ctDNA in early-stage cancer detection.
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Et. al., Md Khaja Mohiddin,. "LAB Scheduling Based Dynamic Multi-Hop Routing & Clustering Algorithm for Efficient Performance of WSN Parameters." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (May 10, 2021): 1495–507. http://dx.doi.org/10.17762/turcomat.v12i11.6072.
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The energy usage constraint is the most critical and desired parameter in wireless sensor networks, and it is the subject of a lot of investigation. With the same motive, survey efforts have been made here, and it is focused on strategies that have been incorporated to increase energy sustainability. Apart from the above, this approach is been evaluated using Cross Layer Network for better output. The results when compared to cross layer network model outperforms good when as compared to the individual layer performance. The routers that are active in WSNs serve an significant role in terms of their position, velocity, and which cluster they are connected to. All nodes may not have the mobility to move from one place to another, for which the MEP algorithm has taken place in this research so as to identify the mobile nodes so that it has to be used for proper communication purpose in comparison with the existing models such as EQSR and ED. Each node-node distance has been evaluated for proper monitoring of the mobile nodes. Only certain nodes with adequate residual energy within themselves can generate the beaconing signal, allowing for precise propagation with minimal energy consumption. Considering the nodes which are having the mobility, the energy consumption can be reduced. It can often be reduced by only taking nodes into account with the appropriate residual energy inside the network. The states of the nodes, like Active, Sleep, Idle, as well as Dead, are also essential for interaction and must be regularly assessed in order to ensure a high PDR value. In contrast to existing protocols such as SMRC and LEACH, effective multi-hop networking is developed depending on DMRC protocol to control each and every node under either of the clusters inside the cluster head. RWS method has also taken into consideration for proper choosing of the routing path along with its estimation. Also, proper scheduling is need to assign the selected nodes for transmission for which LAB scheduling algorithm is implemented in comparison with the existing scheduling algorithms such as BOP and MeshMAC to achieve optimum system throughput. The complete work is been carried out using the NS-II simulation software.
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Bai, Ye, Fei Bo, Wencan Ma, Hongwei Xu, and Dawei Liu. "Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/5755671.
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In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient’s veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May–Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable ( p < 0.005 ). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.
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Coenen, Volker A., Bastian E. Sajonz, Peter C. Reinacher, Christoph P. Kaller, Horst Urbach, and M. Reisert. "A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system." Acta Neurochirurgica 163, no. 10 (June 28, 2021): 2809–24. http://dx.doi.org/10.1007/s00701-021-04890-4.
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Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.
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Siddique, Md Abu Ayub, Wan-Soo Kim, Yeon-Soo Kim, Taek-Jin Kim, Chang-Hyun Choi, Hyo-Jai Lee, Sun-Ok Chung, and Yong-Joo Kim. "Effects of Temperatures and Viscosity of the Hydraulic Oils on the Proportional Valve for a Rice Transplanter Based on PID Control Algorithm." Agriculture 10, no. 3 (March 12, 2020): 73. http://dx.doi.org/10.3390/agriculture10030073.
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This study was conducted to develop a proportional-integral-derivative (PID) control algorithm considering viscosity for the planting depth control system of a rice transplanter using various hydraulic oils at different temperatures and to evaluate the performance of the control algorithm, and compare the performance of the PID control algorithm without considering viscosity and considering viscosity. In this study, the simulation model of the planting depth control system and a PID control algorithm were developed based on the power flow of the rice transplanter (ERP60DS). The primary PID coefficients were determined using the Ziegler-Nichols (Z-N) second method. Routh’s stability criteria were applied to optimize the coefficients. The pole and double zero points of the PID controller were also applied to minimize the sustained oscillations of the responses. The performance of the PID control algorithm was evaluated for three ISO (The International Organization for Standardization) standard viscosity grade (VG) hydraulic oils (VG 32, 46, and 68). The response characteristics were analyzed using statistical method (ANOVA) and Duncan’s multiple range test (DMRT) at a significant level of 0.05 were performed through the statistical software SPSS. The results show that the control algorithm considering viscosity is able to control the pressure of the proportional valve, which is associated with the actuator displacement for various types of hydraulic oils. It was noticed that the maximum pressure was 15.405 bars at 0, 20, 40, 60, 80, and 100 °C for all of the hydraulic oils. The settling time and steady-state errors were 0.45 s at 100 °C for VG 32 and 0% for all of the conditions. The maximum overshoots were found to be 17.50% at 100 °C for VG 32. On the other hand, the PID control algorithm without considering viscosity could not control the planting depth, because the response was slow and did not satisfy the boundary conditions. The PID control algorithm considering viscosity could sufficiently compensate for the nonlinearity of the hydraulic system and was able to perform for any of temperature-dependent viscosity of the hydraulic oils. In addition, the rice transplanter requires a faster response for accurately controlling and maintaining the planting depth. Planting depth is highly associated with actuator displacement. Finally, this control algorithm considering viscosity could be helpful in minimizing the tilting of the seedlings planted using the rice transplanter. Ultimately, it would improve the transplanter performance.
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Wang, Jie, Ying Zuo, Hua Bai, Jianchun Duan, Zhijie Wang, Weihua Li, Jianming Ying, et al. "Genomic and epigenomic profiles to distinguish pulmonary enteric adenocarcinoma from lung metastatic colorectal cancer." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e13528-e13528. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e13528.
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e13528 Background: As an extremely rare variant of lung adenocarcinoma, the diagnosis of pulmonary enteric adenocarcinoma (PEAC) remains challenging in the clinic due to shared morphological and immunohistochemical features with lung metastatic colorectal cancer (mCRC). Current differentiation of PEAC and mCRC mainly relies on clinical history and pathological examination while which still remain risks of misdiagnosis. Due to their distinct treatment regimens, effective molecular markers are essential for accurate diagnosis. However, comprehensive molecular features of PEAC is still poorly understood. Methods: We performed whole-exome sequencing and targeted bisulfite sequencing of 23 PEAC and 20 mCRC and matched normal tissue to improve molecular characterization. For DNA methylation profiling, differentially methylated regions (DMR) were analyzed by comparing PEAC with normal lung tissue and with mCRC. We also trained machine learning methods to distinguish PEAC from mCRC and validated the classifier in an independent cohort with 10 PEAC and 10 mCRC. Results: Mutations of KRAS, APC, and EGFR, alterations of chromosome arms 13q, 14q and 18p were found to be the major differential genetic alterations between PEAC and mCRC (P < 0.05), yet not enough to aid clinical diagnosis. For epigenomic profile, we identified 524 DMRs (false discovery rate ≤0.05) which were further reduced to 30 DMRs according to importance rank by the random forest algorithm. Based on these DMR features, we developed a diagnostic classifier that correctly classified 95.1% of patients in this discovery cohort. We further validated this predictive model in the validation cohort, with a prediction accuracy of 90.0%. We demonstrated its clinical application in two cases with difficulties to diagnosis by traditional methods. Conclusions: We have illustrated the unique genetic and methylation profiles of PEAC and mCRC. Our approach for disease classification may have a substantial impact on diagnostic precision and therapeutic decision for PEAC.
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Torontali, Marianne, Renee Doughman, Brooklyn Chaney, Katie Black, Anthony Asher, Andrew Rupert, Christine Fuller, et al. "EPID-15. THE INTERNATIONAL DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG)/DIFFUSE MIDLINE GLIOMA (DMG) REGISTRY AND REPOSITORY (IDIPGR) EXPANSION." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii321—iii322. http://dx.doi.org/10.1093/neuonc/noaa222.201.
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Abstract Established in April 2012, the mission of the IDIPGR is to provide secure integrated data sets including clinical, pathologic, radiologic and molecular genomics to the research community to promote hypothesis driven research. Over 600 data points per patient are securely stored on a CCHMC constructed web resource and domain using the open-source data mart development framework Harvest (PMID:24303304) (‘Links’). Genomic data is stored in the cloud-enabled VIVA platform and accessed through cross-platform integration and standardization algorithms for comparison across datasets. Features include source identification, data wrangling, and standardization of molecular and phenotypic data (2017), a web-enabled data mart that provides phenotype-genotype query/exploration, along with raw and processed data file downloads to authorized investigators (Harvest, 2017), additional tools for filtering and analysis of genomic datasets at the level of a phenotype, sample, gene, and variant (VIVA, 2017–2018), and uploaded digitized slides (Aperio, 2019). The IDIPGR Repository stores abstracted datasets for >1020 patients with DIPG/DMG, of whom 366 have tumor tissue available through biopsy and/or autopsy, and centrally reviewed and digitized specimens from 124 patients. The Repository contains >5000 radiology studies from >700 patients, with >550 patients centrally reviewed, and genomics data from 80 patients. Currently 27 IDIPGR approved projects utilize these datasets. The DIPG/DMG Registry constructed a robust database platform and integration system that provides the infrastructure to promote highly collaborative, international, hypothesis-driven research. Broadening collaboration among investigators for hypothesis-driven research studies will lead to better classification and more effective treatment of patients with DIPG and DMG.
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Huang, Zhongxin, and Jinhua Cai. "Progress in Brain Magnetic Resonance Imaging of Individuals with Prader–Willi Syndrome." Journal of Clinical Medicine 12, no. 3 (January 29, 2023): 1054. http://dx.doi.org/10.3390/jcm12031054.
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Prader–Willi syndrome (PWS), a rare epigenetic disease mapping the imprinted chromosomal domain of 15q11.2-q13.3, manifests a regular neurodevelopmental trajectory in different phases. The current multimodal magnetic resonance imaging (MRI) approach for PWS focues on morphological MRI (mMRI), diffusion MRI (dMRI) and functional MRI (fMRI) to uncover brain alterations. This technique offers another perspective to understand potential neurodevelopmental and neuropathological processes of PWS, in addition to specific molecular gene expression patterns, various clinical manifestations and metabolic phenotypes. Multimodal MRI studies of PWS patients demonstrated common brain changes in the volume of gray matter, the integrity of the fiber tracts and the activation and connectivity of some networks. These findings mainly showed that brain alterations in the frontal reward circuit and limbic system were related to molecular genetics and clinical manifestations (e.g., overwhelming eating, obsessive compulsive behaviors and skin picking). Further exploration using a large sample size and advanced MRI technologies, combined with artificial intelligence algorithms, will be the main research direction to study the structural and functional changes and potential pathogenesis of PWS.
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Phan, X. V., L. Ferro-Famil, M. Gay, Y. Durand, M. Dumont, S. Morin, S. Allain, G. D'Urso, and A. Girard. "3D-VAR multilayer assimilation of X-band SAR data into a detailed snowpack model." Cryosphere Discussions 7, no. 5 (October 2, 2013): 4881–912. http://dx.doi.org/10.5194/tcd-7-4881-2013.
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Abstract. We introduce a variational data assimilation scheme to assimilate X-band Synthetic Aperture Radar (SAR) data into a snowpack evolution model. The structure properties of a snowpack, such as snow density and grain optical diameter of each layer, are simulated over a period of time by the snow metamorphism model Crocus, fed by the local reanalysis SAFRAN at a French alpine location. These parameters are used as inputs of an Electromagnetic Backscattering Model (EBM) based on Dense Media Radiative Transfer (DMRT) theory, which calculates the simulated total backscattering coefficient. Next, 3D-VAR data assimilation is implemented in order to minimize the discrepancies between model simulations and observations obtained from SAR acquisitions, by modifying the parameters of a multilayer snowpack calculated by Crocus. The algorithm then reinitializes Crocus with the optimized snowpack structure properties, and therefore allows it to continue the simulation of snowpack evolution where adjustments based on remote sensing data has been taken into account. Results obtained using TerraSAR-X acquisitions on Argentière Glacier (Mont-Blanc massif, French Alps) show the high potential of this method for improving snow cover simulation.