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State of the art paper

Yan Zhang, Li An, Jun Shen
PURPOSE: Numerical simulations of three-dimensionally localized MRS spectra have been very time-consuming for multi-spin systems because the current state-of-the-art requires computation of a large ensemble of spins pixel-by-pixel in three dimensional space. This paper describes a highly accelerated technique for computing spatially localized MRS spectra using the full solution to the Liouville-von Neumann equation. METHODS: The time evolution of spatially localized multispin density matrix as the full solution to the Liouville-von Neumann equation was analyzed...
May 26, 2017: Medical Physics
Ignacio Ponzoni, Víctor Sebastián-Pérez, Carlos Requena-Triguero, Carlos Roca, María J Martínez, Fiorella Cravero, Mónica F Díaz, Juan A Páez, Ramón Gómez Arrayás, Javier Adrio, Nuria E Campillo
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances...
May 25, 2017: Scientific Reports
Pietro Invernizzi', Annarosa Floreani, Marco Carbone, Marco Marzioni, Antonio Craxi, Luigi Muratori, Umberto Vespasiani Gentilucci, Ivan Gardini, Antonio Gasbarrini, Paola Krugeri, Francesco Saverio Mennini, Virginia Ronco, Elena Lanati, Pier Luigi Canonico, Domenico Alvaro
Primary Biliary Cholangitis, previously known as Primary Biliary Cirrhosis, is a rare disease, which mainly affects women in their fifth to seventh decades of life. It is a chronic autoimmune disease characterized by a progressive damage of interlobular bile ducts leading to ductopenia, chronic cholestasis and bile acids retention. Even if the disease usually presents a long asymptomatic phase and a slow progression, in many patients it may progress faster toward cirrhosis and its complications. The 10year mortality is greater than in diseases such as human immunodeficiency virus/Hepatitis C Virus coinfection and breast cancer...
May 8, 2017: Digestive and Liver Disease
Nora Moumjid, Julien Carretier, Giovanna Marsico, François Blot, Christine Durif-Bruckert, Franck Chauvin
In this paper we present the evolution of shared decision-making since the mid-nineties in terms of legislation, official statements and guidelines. We outline the goals and declarations of the French Ministry of Health and the French National Authority for Health, for whom informing patients and shared decision-making are central concerns. Finally, we discuss research projects and clinical initiatives in shared decision-making in France and provide a general overview of progress and barriers to progress.
May 22, 2017: Zeitschrift Für Evidenz, Fortbildung und Qualität Im Gesundheitswesen
Hamid Hosseinzadegan, Danesh K Tafti
The paper reviews the state-of-the-art in computational modeling of thrombus formation and growth and related phenomena including platelet margination, activation, adhesion, and embolization. Presently, there is a high degree of empiricism in the modeling of thrombus formation. Based on the experimentally observed physics, the review gives useful strategies for predicting thrombus formation and growth. These include determining blood components involved in atherosclerosis, effective blood viscosity, tissue properties, and methods proposed for boundary conditions...
May 22, 2017: Biotechnology and Bioengineering
Wenzhang Zhuge, Chenping Hou, Yuanyuan Jiao, Jia Yue, Hong Tao, Dongyun Yi
In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research. Previous multi-view subspace methods aim to learn multiple subspace representation matrices simultaneously and these learning task for different views are treated equally...
2017: PloS One
Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang
In this paper, we propose to detect the running applications in a server by classifying the observed power consumption series for the purpose of data center energy consumption monitoring and analysis. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning-based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbor and long short term memory (LSTM) neural network...
May 24, 2017: IEEE Transactions on Cybernetics
Sezer Karaoglu, Ran Tao, Jan C van Gemert, Theo Gevers
This work focuses on fine-grained object classification using recognized scene text in natural images. While the state-of-the-art relies on visual cues only, this paper is the first work which proposes to combine textual and visual cues. Another novelty is the textual cue extraction. Unlike the state-of-the-art text detection methods, we focus more on the background instead of text regions. Once text regions are detected, they are further processed by two methods to perform text recognition i.e. ABBYY commercial OCR engine and a state-of-the-art character recognition algorithm...
May 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Gillian Hsieh, Rob Bierman, Linda Szabo, Alex Gia Lee, Donald E Freeman, Nathaniel Watson, E Alejandro Sweet-Cordero, Julia Salzman
Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1...
May 24, 2017: Nucleic Acids Research
Licheng Liu, Shutao Li, C L Philip Chen
Recently, the locality linear coding (LLC) has attracted more and more attentions in the areas of image processing and computer vision. However, the conventional LLC with real setting is just designed for the grayscale image. For the color image, it usually treats each color channel individually or encodes the monochrome image by concatenating all the color channels, which ignores the correlations among different channels. In this paper, we propose a quaternion-based locality-constrained coding (QLC) model for color face hallucination in the quaternion space...
May 23, 2017: IEEE Transactions on Cybernetics
Dong Huang, Chang-Dong Wang, Jian-Huang Lai
Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering...
May 23, 2017: IEEE Transactions on Cybernetics
Xiaojun Chen, Joshua Zhexue Huang, Qingyao Wu, Min Yang
Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be possible correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data. In this paper, we propose a novel algorithm, called Subspace Weighting Co-Clustering (SWCC), for high dimensional gene expression data. In SWCC, a gene subspace weight matrix is introduced to identify the contribution of gene objects in distinguishing different sample clusters...
May 18, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Bige D Unluturk, M Siblee Islam, Sasitharan Balasubramaniam, Stepan Ivanov
The progress of molecular communication is tightly connected to the progress of nanomachine design. State-of-the-art states that nanomachines can be built either from novel nanomaterials by the help of nanotechnology or they can be built from living cells which are modified to function as intended by synthetic biology. With the growing need of biomedical applications of MC, we focus on developing bio-compatible communication systems by engineering the cells to become MC nanomachines. Since this approach relies on modifying cellular functions, the improvements in the performance can only be achieved by integrating new biological properties...
May 23, 2017: IEEE Transactions on Nanobioscience
Jianliang Gao, Bo Song, Weimao Ke, Xiaohua Hu
Coverage and consistency are two most considered metrics to evaluate the effectiveness of network alignment. But they are a pair of contradictory evaluation metrics in PPI network alignment. It is difficult, if not impossible, to achieve high coverage and consistency simultaneously. Furthermore, existing methods of multiple PPI network alignment mostly ignore k-coverage or k-consistency, where k indicates the number of aligned species. In this paper, we propose BalanceAli, a novel approach for global alignment of multiple PPI networks that achieves high k-coverage and k-consistency simultaneously...
May 18, 2017: IEEE Transactions on Nanobioscience
Jing-Ming Guo, Jin-Yu Syue, Vincent Radzicki, Hua Lee
Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models...
May 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Wei Jia, Bob Zhang, Jingting Lu, Yihai Zhu, Yang Zhao, Wangmeng Zuo, Haibin Ling
Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilized all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Liansheng Zhuang, Zihan Zhou, Shenghua Gao, Jingwen Yin, Zhouchen Lin, Yi Ma
In the literature, most existing graph-based semi- supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR)...
May 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yuru Pei, Gengyu Ma, Gui Chen, Xiaoyun Zhang, Tianmin Xu, Hongbin Zha
OBJECTIVE: The superimposition of cone-beam computed tomography (CBCT) images is an essential step to evaluate shape variations of pre and postorthodontic operations due to pose variations and the bony growth. The aim of this paper is to present and discuss the latest accomplishments in voxel-based craniofacial CBCT superimpositions along with structure discriminations. METHODS: We propose a CBCT superimposition method based on joint embedding of subsets extracted from CBCT images...
June 2017: IEEE Transactions on Bio-medical Engineering
Joji Abraham, Kim Dowling, Singarayer Florentine
One of the significant economic benefits to communities around the world of having pristine forest catchments is the supply of substantial quantities of high quality potable water. This supports a saving of around US$ 4.1 trillion per year globally by limiting the cost of expensive drinking water treatments and provision of unnecessary infrastructure. Even low levels of contaminants specifically organics and metals in catchments when in a mobile state can reduce these economic benefits by seriously affecting the water quality...
May 19, 2017: Science of the Total Environment
Majid Komeili, Wael Louis, Narges Armanfard, Dimitrios Hatzinakos
Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions...
May 16, 2017: IEEE Transactions on Cybernetics
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