Genetic mapping of complex traits by minimizing integrated square errors

<p>Abstract</p> <p>Background</p> <p>Genetic mapping has been used as a tool to study the genetic architecture of complex traits by localizing their underlying quantitative trait loci (QTLs). Statistical methods for genetic mapping rely on a key assumption, that is, traits obey a parametric distribution. However, in practice real data may not perfectly follow the specified distribution.</p> <p>Results</p> <p>Here, we derive a robust statistical approach for QTL mapping that accommodates a certain degree of misspecification of the true model by incorporating integrated square errors into the genetic mapping framework. A hypothesis testing is formulated by defining a new test statistics - energy difference.</p> <p>Conclusions</p> <p>Simulation studies were performed to investigate the statistical properties of this approach and compare these properties with those from traditional maximum likelihood and non-parametric QTL mapping approaches. Lastly, analyses of real examples were conducted to demonstrate the usefulness and utilization of the new approach in a practical genetic setting.</p

Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography

Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.National Science and Technology Major Project of China [2017ZX05008-003-021]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB10030601]; Youth Innovation Promotion Association of the Chinese Academy of Sciences [2016063]; US Civilain Research and Development Foundation (CRDF) under the award: Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt [DAA2-15-61224-1]; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City6 month embargo; published online: 23 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice

Dual Activities of ACC Synthase: Novel Clues Regarding the Molecular Evolution of Acs Genes

Ethylene plays profound roles in plant development. The rate-limiting enzyme of ethylene biosynthesis is 1-aminocyclopropane-1-carboxylate (ACC) synthase (ACS), which is generally believed to be a single-activity enzyme evolving from aspartate aminotransferases. Here, we demonstrate that, in addition to catalyzing the conversion of S-adenosyl-methionine to the ethylene precursor ACC, genuine ACSs widely have Cβ-S lyase activity. Two N-terminal motifs, including a glutamine residue, are essential for conferring ACS activity to ACS-like proteins. Motif and activity analyses of ACS-like proteins from plants at different evolutionary stages suggest that the ACC-dependent pathway is uniquely developed in seed plants. A putative catalytic mechanism for the dual activities of ACSs is proposed on the basis of the crystal structure and biochemical data. These findings not only expand our current understanding of ACS functions but also provide novel insights into the evolutionary origin of ACS genes

A Reconstruction Method for Hyperspectral Remote Sensing Reflectance in the Visible Domain and Applications

A reconstruction method was developed for hyperspectral remote sensing reflectance (Rrs)data in the visible domain (400–700 nm) based on in situ observations. A total of 2,647 Rrs spectra were collected over a wide variety of water environments including open ocean, coastal and inland waters. Ten schemes with different band numbers (6 to 15) were tested based on a nonlinear model. It was found that the accuracy of the reconstruction increased with the increase of input band numbers. Eight of these schemes met the accuracy criterion with the mean absolute error (MAE) and mean relative error (MRE)values between reconstructed and in situ Rrs less than 0.00025 sr-1 and 5%, respectively. We chose the eight-band scheme for further evaluation because of its decent performance. The results revealed that the parameterization derived by the eight-band scheme was efficient for restoring Rrs spectra from different water bodies. In contrast to the previous studies that used a linear model with 15 spectral bands, the nonlinear model with the eight-band scheme yielded a comparable reconstruction performance. The MAE andMRE values were generally less than 0.00016 sr-1 and 3% respectively; much lower than the uncertainties in satellite-derived Rrs products. Furthermore, a preliminary experiment of this method on the data from the Hyperspectral Imager for the Coastal Ocean (HICO) showed high potential in the future applications for reconstructing Rrs spectra from space-borne optical sensors. Overall, the eight-band scheme with our non-linear model was proven to be optimal for hyperspectral Rrs reconstruction in the visible domain

Nuclear Export and Plasma Membrane Recruitment of the Ste5 Scaffold Are Coordinated with Oligomerization and Association with Signal Transduction Components

The Ste5 scaffold activates an associated mitogen-activated protein kinase cascade by binding through its RING-H2 domain to a Gβγ dimer (Ste4/Ste18) at the plasma membrane in a recruitment event that requires prior nuclear shuttling of Ste5. Genetic evidence suggests that Ste5 must oligomerize to function, but its impact on Ste5 function and localization is unknown. Herein, we show that oligomerization affects Ste5 activity and localization. The majority of Ste5 is monomeric, suggesting that oligomerization is tightly regulated. Increasing the pool of Ste5 oligomers increases association with Ste11. Remarkably, Ste5 oligomers are also more efficiently exported from the nucleus, retained in the cytoplasm by Ste11 and better recruited to the plasma membrane, resulting in constitutive activation of the mating mitogen-activated protein kinase cascade. Coprecipitation tests show that the RING-H2 domain is the key determinant of oligomerization. Mutational analysis suggests that the leucine-rich domain limits the accessibility of the RING-H2 domain and inhibits export and recruitment in addition to promoting Ste11 association and activation. Our results suggest that the major form of Ste5 is an inactive monomer with an inaccessible RING-H2 domain and Ste11 binding site, whereas the active form is an oligomer that is more efficiently exported and recruited and has a more accessible RING-H2 domain and Ste11 binding site

Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with self-feedback recursive structure (IT2FNN-SFR STSMC) is proposed in this paper. IT2FNN has an uncertain membership function, which can enhance the nonlinear ability and robustness of the network. The historical information will be stored and utilized by the self-feedback recursive structure (SFR) at runtime. Therefore, the novel IT2FNN-SFR is designed to improve the dynamic approximation effect of the neural network and reduce the dependence of the controller on the actual mathematical model. The adaptive rate of each weight of the neural network is designed by the Lyapunov method and gradient descent (GD) algorithm to ensure the convergence and stability of the system. Super-twisting sliding mode control (STSMC) has strong robustness, which can effectively reduce system chattering, and improve control accuracy and system performance. The gain of the integral term in the STSMC is set as a constant, and the other gain is changed adaptively whose adaptive rate is deduced through the stability proof of the neural network, which greatly reduces the difficulty of parameter adjustment. The harmonic suppression ability of the designed control strategy is verified by simulation experiments

Image Denoising And Segmentation Via Nonlinear Diffusion

. Image dnoising and segmentation are fundamental problems in the field of image processing and computer vision with numerous applications. In this paper we present a novel nonlinear diffusion model augmented with reactive terms that yields quality denoising and segmentation results on a variety of images. We present a proof for the existence, uniqueness and stability of the viscosity solution of this PDE-based model. To achieve a faster implementation, we embed the the model in a scale space and the solution is achieved via a dynamic system governed by a coupled system of first order differential equations. The dynamic system finds the solution at a coarse scale and tracks it continuously to a desired fine scale. We implement this scale-space tracking using a multigrid technique and demonstrate the smoothing and segmentation results on several images. Key words. Nonlinear Diffusion, Image Processing, Segmentation, PDEs, Scale-space 1. Introduction. Image denoising and segmentation ..

Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least 38%, and the computational efficiency in the billing phase is improved by at least 40 times