Modeling and Reconstruction of Mixed Functional and Molecular Patterns

Functional medical imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes in living tissue. Recent research aims to dissect the distribution or expression of multiple biomarkers associated with disease progression or response, where the signals often represent a composite of more than one distinct source independent of spatial resolution. Formulating the task as a blind source separation or composite signal factorization problem, we report here a statistically principled method for modeling and reconstruction of mixed functional or molecular patterns. The computational algorithm is based on a latent variable model whose parameters are estimated using clustered component analysis. We demonstrate the principle and performance of the approaches on the breast cancer data sets acquired by dynamic contrast-enhanced magnetic resonance imaging

Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

RGB-NIR image categorization with prior knowledge transfer

Abstract Recent development on image categorization, especially scene categorization, shows that the combination of standard visible RGB image data and near-infrared (NIR) image data performs better than RGB-only image data. However, the size of RGB-NIR image collection is often limited due to the difficulty of acquisition. With limited data, it is difficult to extract effective features using the common deep learning networks. It is observed that humans are able to learn prior knowledge from other tasks or a good mentor, which is helpful to solve the learning problems with limited training samples. Inspired by this observation, we propose a novel training methodology for introducing the prior knowledge into a deep architecture, which allows us to bypass the burdensome labeling large quantity of image data to meet the big data requirements in deep learning. At first, transfer learning is adopted to learn single modal features from a large source database, such as ImageNet. Then, a knowledge distillation method is explored to fuse the RGB and NIR features. Finally, a global optimization method is employed to fine-tune the entire network. The experimental results on two RGB-NIR datasets demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art multi-modal image categorization methods.https://deepblue.lib.umich.edu/bitstream/2027.42/146762/1/13640_2018_Article_388.pd

Strange Nonchaotic Attractors In A Periodically Forced Piecewise Linear System With Noise

Acknowledgments This work is supported by the National Natural Science Foundation of China (NNSFC) (Nos. 12072291, 11732014 and 12172306)Peer reviewedPostprin

Online sensorless position estimation for switched reluctance motors using one current sensor

This paper proposes an online sensorless rotor position estimation technique for switched reluctance motors (SRMs) using just one current sensor. It is achieved by first decoupling the excitation current from the bus current. Two phase-shifted pulse width modulation signals are injected into the relevant lower transistors in the asymmetrical half-bridge converter for short intervals during each current fundamental cycle. Analog-to-digital converters are triggered in the pause middles of the dual pulse to separate the bus current for excitation current recognition. Next, the rotor position is estimated from the excitation current, by a current-rise-time method in the current-chopping-control mode in a low-speed operation and a current-gradient method in the voltage-pulse-control mode in a high-speed operation. The proposed scheme requires only a bus current sensor and a minor change to the converter circuit, without a need for individual phase current sensors or additional detection devices, achieving a more compact and cost-effective drive. The performance of the sensorless SRM drive is fully investigated. The simulation and experiments on a 750-W three-phase 12/8-pole SRM are carried out to verify the effectiveness of the proposed scheme

Online sensorless position estimation for switched reluctance motors using one current sensor

This paper proposes an online sensorless rotor position estimation technique for switched reluctance motors (SRMs) using just one current sensor. It is achieved by first decoupling the excitation current from the bus current. Two phase-shifted pulse width modulation signals are injected into the relevant lower transistors in the asymmetrical half-bridge converter for short intervals during each current fundamental cycle. Analog-to-digital converters are triggered in the pause middles of the dual pulse to separate the bus current for excitation current recognition. Next, the rotor position is estimated from the excitation current, by a current-rise-time method in the current-chopping-control mode in a low-speed operation and a current-gradient method in the voltage-pulse-control mode in a high-speed operation. The proposed scheme requires only a bus current sensor and a minor change to the converter circuit, without a need for individual phase current sensors or additional detection devices, achieving a more compact and cost-effective drive. The performance of the sensorless SRM drive is fully investigated. The simulation and experiments on a 750-W three-phase 12/8-pole SRM are carried out to verify the effectiveness of the proposed scheme

On the Order Hereditary Closure Preserving Sum Theorem

[EN] The main purpose of this paper is to prove the following two theorems, an order hereditary closure preserving sum theorem and an hereditary theorem: (1) If a topological property P satisfies (Σ′) and is closed hereditary, and if V is an order hereditary closure preserving open cover of X and each V ϵ V is elementary and possesses P, then X possesses P. (2) Let a topological property P satisfy (Σ′) and (β), and be closed hereditary. Let X be a topological space which possesses P. If every open subset G of X can be written as an order hereditary closure preserving (in G) collection of elementary sets, then every subset of X possesses P.Gong, J.; Reilly, IL. (2007). On the Order Hereditary Closure Preserving Sum Theorem. Applied General Topology. 8(2):267-272. doi:10.4995/agt.2007.1892.SWORD2672728

Demonstration of membrane distillation on textile waste water: assessment of long term performance, membrane cleaning and waste heat integration

© 2017 The Royal Society of Chemistry. This work reports outcomes of a pilot trial and practical assessment of direct contact membrane distillation (DCMD) towards achieving zero liquid discharge at a textile manufacturing plant. Treatment of textile wastewater is difficult due primarily to the complexity of textile processing and the wastewater produced. Combined effluent from the site, either untreated or treated with the site\u27s existing flocculation and biological processes, were considered as the feeds to the MD testing. Initial bench scale studies found rapid membrane wetting appeared to be avoided by the novel use of foam fractionation on the untreated effluent, or by using the conventionally treated effluent. The trial was conducted on treated effluent using fractionation on a side stream within the MD process, and no wetting was observed over the entire 3 month trial duration. The flux of the 6.4 m2 membrane module started at 5 L m-2 h-1 and declined to 2 L m-2 h-1 after more than 65 days. Caustic cleaning effectively restored flux to 4 L m-2 h-1. A 41-fold increase in feed concentration was verified by sulphate measurements, increasing from 567 mg L-1 to 23 000 mg L-1. After concentrating in the hot cycle, all ammonia entering the DCMD plant from the feedwater was found to evolve into the permeate, but non-volatile sulphate rejection was >99.9%. Water recovery at the end of the trial was 91.6%. A plant integration assessment found that zero liquid discharge would be feasible if saline waste streams were isolated and reverse osmosis processes were coupled with MD harnessing waste heat. MD application to current and future treatment scenarios with waste heat integration to textile processing appears viable