The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data. The changes in the TF binding intensities between the altered sequence and the reference sequence reflect the degree of functional impact for the variant. This enables us to develop the tool DeFine (Deep learning based Functional impact of non-coding variants evaluator, http://define.cbi.pku.edu.cn) with improved performance for assessing the functional impact of non-coding variants including SNPs and indels. DeFine accurately identifies the causal functional non-coding variants from disease-associated variants in GWAS. DeFine is an effective and easy-to-use tool that facilities systematic prioritization of functional non-coding variants.

We describe a time-domain (pulsed) eddy-current technique for determining the thickness and conductivity of conductive coatings on metal plates. The pulsed eddy-current instrument records the transient current induced in an absolute, air-cored coil placed next to a layered sample and excited with a step-function change in voltage. Signals are digitized with 16-bit resolution at a sampling rate of 1 megasamples per second, and the excitation is repeated at a rate of 1 kHz. The instrument displays the difference in the transient current measured on the substrate and on the substrate plus coating. We measured pulsed eddy-current signals for a series of metal foils of varying thickness placed over 1 cm thick metal plates. Seven combinations of foil and substrate metals were studied including pure aluminum, copper, and titanium foils over substrates of aluminum, titanium alloy, and stainless steel. We report results for three types of samples: aluminum foils on Ti–6Al–4V substrate, titanium foils on 7075 aluminum alloys, and aluminum foils on AISI 304 stainless steel. Foil thickness ranged from 0.04–1.00 mm. We found that three features of the signal—the peak height, the time of occurrence of the first peak, and a characteristic zero-crossing time—depend sensitively upon the thickness of the layers and the relative electrical conductivity of coating and substrate. Theoretical calculations were compared to the measurements. Absolute agreement between calculated and measured signals was, in most cases, within 3%. No calibration with respect to artifact standards was used. Finally, a feature-based rapid inversion method was developed and used to infer the thickness and conductivity of the layers. The accuracy of the inversion depends upon the thickness of the layer and the contrast in conductivity between layer and substrate. For the materials studied the thickness could be determined within 13%, while the error in determining conductivity was 20%–30%. The time-domain method is much simpler and hundreds of times faster than the frequency-domain method previously reported by Moulder et al. [Rev. Sci. Instrum. 63, 3455 (1992)].

Empirical mode decomposition (EMD) is a signal processing method used to extract intrinsic mode functions (IMFs) from a complicated signal. For a measurement with two or more correlated inputs, finding and capturing the correlated IMFs is a critical challenge that must be confronted. In this paper, a new correlated EMD method is proposed. The cross-correlation method was employed to determine dependence between the IMFs. To verify feasibility, an analysis was performed on simulated test signals and practically measured partial discharge (PD) signals collected from several acoustic emission sensors. At the surface of the gas-insulated transmission line, the PD signal arrived at the AE sensors with varying time delays and unique mechanism vibrations. Following an abnormal detection using the standard-deviation variation, the PD signal and the background signal of each sensor were applied using the correlated-EMD method. A twice correlated-EMD calculation was applied to the signals for the purpose of noise elimination. In addition, the unwanted low-frequency IMFs induced from the EMD calculations were excluded. The experimental results reveal that the correlated-EMD method performs well on both selecting and denoising the correlated IMFs. The results further provide analysis on correlated-input applications with a precise signal completely induced from the disturbance.

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Wavelet frames have been successfully applied to various image restoration problems, such as denoising, inpainting, and deblurring. However, they are rarely used in geometric applications, except for the recent work of [B. Dong, ]. Motivated by the theoretical connection between wavelet frame based and total variation based image restoration models recently established in [we propose here a convex multiphase segmentation model based on wavelet frame transform. The proposed model allows us to automatically identify complex tubular structures, including blood vessels, leaf vein systems, etc. Numerical results show that our method can extract more details than existing variational methods especially when the image contains different scales of structures. The proposed method is parallelized, and its efficiency is further improved by a graphics processing unit implementation. In addition, we analyze the connection between solutions of the convexified model and the original binary constrained model. Introduction.Multiphase image segmentation, or multiphase labeling, is the process of partitioning an image into multiple regions with respect to specific goals and applications. It is a basic but very important image analysis task that has been extensively investigated for many years. Among all the models, regularization based variational models have proved to be especially successful. Variational models started with the classic work by Mumford and Shah [42] and active contour models [33,39]. Later, the Chan-Vese active contour model [19] and its variants based on level sets and total variation [47] were proposed to improve earlier results in terms of both segmentation accuracy and computation efficiency; see [17,19,34,35]. However, the quality of the Chan-Vese model relies on initializations due to the nonconvexity of the model. The Mumford-Shah model is a special case of the classical Potts model in a discrete setting. The general Potts model consists of solving the image segmentation problem by minimizing a sum of the lengths of the boundaries of the regions and data fidelity, and it is known that solving the Potts model is NP hard for multiphase cases [5]. Many kinds of

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A measurement technique using the swept-frequency eddy current ͑SFEC͒ method for determining the thickness, conductivity, and permeability of metallic coatings on metal substrates for the case when either coating, substrate, or both are magnetic was developed. This technique involved using the empirically determined permeability of the metals as input to the model calculation. This technique is demonstrated for nickel layers ͑25-200 m͒ over copper substrates, copper layers ͑25-200 m͒ over nickel substrates, and zinc layers ͑50-400 m͒ over steel substrates. The electrical impedance was measured for air-core coils in the presence and absence of the layer using a 4194A Hewlett-Packard impedance analyzer. An analytic closed-form solution for calculating the impedance of a cylindrical air-core probe over a layered metallic half-space is presented. The determination of coating thickness and conductivity and permeability of the metals is based on the comparison of the data taken with air-core coils and theoretical calculation that using the closed-form solution developed. Most of the cases studied show experiment and theory agreeing fairly well, within 10%, with no adjustable parameters. The physical phenomena of eddy currents interaction with the coated magnetic metals are also discussed.