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Lu Wang, Shilong Li, Biran Zhang, Yuzhou Qin, Ziao Tian, Yangfu Fang, Yonglei Li, Zhaowei Liu, Yongfeng Mei
We demonstrate hyperbolic metamaterials (HMMs) on curved surface for an efficient outcoupling of non-radiative modes, which lead to an enhanced spontaneous emission. Those high-wavevector plasmonic modes can propagate along the curved structure and emit into the far-field, realizing a directional light emission with maximal fluorescent intensity. Detailed simulations disclose a high Purcell factor and a spatial power distribution in the curved HMM, which agrees with the experimental result. Our work presents remarkable enhancing capability in both Purcell factor and emission intensity, which could suggest unique structure design in metamaterials for potential application in e...
February 13, 2018: ACS Applied Materials & Interfaces
Mai Higuchi, Junichi Takahara
Hyperbolic metamaterials (HMMs) show great promise in photonics applications because their unconventional open isofrequency surface permits enlargement of wavenumbers without limitation. Although optical behaviors in HMMs can be macroscopically described by theoretical calculations with the effective medium approximation (EMA), neglect of microscopic phenomena in each layer leads to discrepancies from exact numerical results. We clarify the origin of bulk propagating waves in HMMs and we show that they can be classified into two modes: long- and short-range surface-plasmon-based coupled modes (LRSP and SRSP, respectively)...
January 22, 2018: Optics Express
Kirubin Pillay, Anneleen Dereymaeker, Katrien Jansen, Gunnar Naulaers, Sabine Van Huffel, Maarten De Vos
<i>Objective</i>. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel Electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader Quiet Sleep (QS) and Active Sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development...
January 30, 2018: Journal of Neural Engineering
Jingheng Cai, Ming Ouyang, Kai Kang, Xinyuan Song
Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities...
January 11, 2018: Multivariate Behavioral Research
Hui-Ju Kao, Shun-Long Weng, Kai-Yao Huang, Fergie Joanda Kaunang, Justin Bo-Kai Hsu, Chien-Hsun Huang, Tzong-Yi Lee
BACKGROUND: Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson's disease, and Alzheimer's disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures...
December 21, 2017: BMC Systems Biology
Kenko Fujii, Gauthier Gras, Antonino Salerno, Guang-Zhong Yang
While minimally invasive surgery offers great benefits in terms of reduced patient trauma, bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic challenges. Laparoscopic surgery requires the surgeon to bimanually control surgical instruments during the operation. A dedicated assistant is thus required to manoeuvre the camera, which is often difficult to synchronise with the surgeon's movements. This article introduces a robotic system in which a rigid endoscope held by a robotic arm is controlled via the surgeon's eye movement, thus forgoing the need for a camera assistant...
November 28, 2017: Medical Image Analysis
Lukas Zimmermann, Andrew Stephens, Seung-Zin Nam, David Rau, Jonas Kübler, Marko Lozajic, Felix Gabler, Johannes Söding, Andrei N Lupas, Vikram Alva
The MPI Bioinformatics Toolkit ( is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2...
December 16, 2017: Journal of Molecular Biology
Wisnu Wiradhany, Mark R Nieuwenstein
In the original article, the number of HMMs and LMMs who took part in the first study was reported to have been 13and 10, respectively (p. 2624).
December 13, 2017: Attention, Perception & Psychophysics
Ananth Prakash, Matt Jeffryes, Alex Bateman, Robert D Finn
Protein sequence similarity search is one of the most commonly used bioinformatics methods for identifying evolutionarily related proteins. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software, which in the latest version (v3.1) uses a combination of sophisticated acceleration heuristics and mathematical and computational optimizations to enable the use of profile hidden Markov models (HMMs) for sequence analysis...
December 8, 2017: Current Protocols in Bioinformatics
Tong Liu, Zhuo-Qian Liang
This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance...
December 5, 2017: Sensors
Mai Desouky, Ahmed M Mahmoud, Mohamed A Swillam
Noble Metals such as Gold and Silver demonstrated for mid IR metamaterials have suffered many obstacles such as: high losses and lack of tunability. The application of doped semiconductors has allowed overcoming the tunability restriction, besides, possessing lower losses as compared to metals. In addition, doped semiconductors have small magnitude of negative real permittivity which is required to realize mid IR Hyperbolic Metamaterials (HMMs). We theoretically demonstrate super focusing based on an all Semiconductor planar HMM using InAs heterostructure...
November 10, 2017: Scientific Reports
Daniel H Haft, Michael DiCuccio, Azat Badretdin, Vyacheslav Brover, Vyacheslav Chetvernin, Kathleen O'Neill, Wenjun Li, Farideh Chitsaz, Myra K Derbyshire, Noreen R Gonzales, Marc Gwadz, Fu Lu, Gabriele H Marchler, James S Song, Narmada Thanki, Roxanne A Yamashita, Chanjuan Zheng, Françoise Thibaud-Nissen, Lewis Y Geer, Aron Marchler-Bauer, Kim D Pruitt
The Reference Sequence (RefSeq) project at the National Center for Biotechnology Information (NCBI) provides annotation for over 95 000 prokaryotic genomes that meet standards for sequence quality, completeness, and freedom from contamination. Genomes are annotated by a single Prokaryotic Genome Annotation Pipeline (PGAP) to provide users with a resource that is as consistent and accurate as possible. Notable recent changes include the development of a hierarchical evidence scheme, a new focus on curating annotation evidence sources, the addition and curation of protein profile hidden Markov models (HMMs), release of an updated pipeline (PGAP-4), and comprehensive re-annotation of RefSeq prokaryotic genomes...
November 3, 2017: Nucleic Acids Research
M Farokhzadi, A Maleki, A Fallah, S Rashidi
Estimating the elbow angle using shoulder data is very important and valuable in Functional Electrical Stimulation (FES) systems which can be useful in assisting C5/C6 SCI patients. Much research has been conducted based on the elbow-shoulder synergies. The aim of this study was the online estimation of elbow flexion/extension angle from the upper arm acceleration signals during ADLs. For this, a three-level hierarchical structure was proposed based on a new approach, i.e. 'the movement phases'. These levels include Clustering, Recognition using HMMs and Angle estimation using neural networks...
September 2017: Journal of Biomedical Physics & Engineering
Konstantinos Kyritsis, Christina Lefkothea Tatli, Christos Diou, Anastasios Delopoulos
Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Raul I Ramos-Garcia, Edward Sazonov, Stephen Tiffany
Previous studies with the Personal Automatic Cigarette Tracker (PACT) wearable system have found that smoking presents a distinct temporal breathing pattern, which might be well-suited for recognition by hidden Markov models (HMMs). In this work, we explored the feasibility of using HMMs to characterize the temporal information of smoking inhalations contained in the respiratory signals such as tidal volume, airflow, and the signal from the hand-to-mouth proximity sensor. Left-to-right HMMs were built to classify smoking and non-smoking inhalations using either only the respiratory signals, or both respiratory and hand proximity signals...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Hong Su, Zhou Li, Samuel Selorm Fiati Kenston, Hongbo Shi, Yafei Wang, Xin Song, Yuanliang Gu, Tabatha Barber, Joni Aldinger, Baobo Zou, Min Ding, Jinshun Zhao, Xialu Lin
The systemic toxicity of different combinations of heavy metal mixtures (HMMs) was studied according to equivalent proportions of the eight most common detectable heavy metals found in fish consumption in the Ningbo area of China. The ion mass proportions of Zn, Cu, Mn, Cr, Ni, Cd, Pb, and Hg were 1070.0, 312.6, 173.1, 82.6, 30.0, 13.3, 6.6, and 1.0, respectively. In this study, 10 experimental groups were set as follows: M8 (Pb + Cd + Hg + Ni + Cu + Zn + Mn + Cr); M5 (Pb + Cd + Hg + Ni + Cr); M4A (Pb + Cd + Hg + Ni); M4B (Cu + Zn + Mn + Cr); M3 (Cu + Zn + Mn); Cr; Cu; Zn; Mn; and control...
October 1, 2017: International Journal of Environmental Research and Public Health
Carlos A Abanto-Valle, Roland Langrock, Ming-Hui Chen, Michel V Cardoso
In this article, we introduce a likelihood-based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions (Abanto-Valle et al., 2012). Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. The method is based on the proposal of Langrock et al. (2012) and makes explicit the useful link between HMMs and SVM models with SMN distributions...
July 2017: Applied Stochastic Models in Business and Industry
Lina Abou-Abbas, Chakib Tadj, Hesam Alaie Fersaie
The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed. The contribution of various additional time- and frequency-domain features to increase the robustness of a Gaussian mixture model/hidden Markov model (GMM/HMM)-based cry segmentation system in noisy environments is studied...
September 2017: Journal of the Acoustical Society of America
Hanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Le Song, Xin Gao
Motivation: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Results: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape...
November 15, 2017: Bioinformatics
Weiming Hu, Guodong Tian, Yongxin Kang, Chunfeng Yuan, Stephen Maybank
In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions...
September 25, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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