Monogenic Components Research Articles

030 Genetics of endometriosis

Endometriosis is a common condition with a heritability (disease variance attributable to genetics) estimated at around 50%. Family-based studies have indicated that it is unlikely that familial endometriosis has a monogenic component. Rather, endometriosis is a complex, polygenic disease with many variants across many genes and environmental factors contributing to risk [ [1] Zondervan T. Becker C.M. Missmer S.A. Endometriosis. N Engl J Med. 2020; 382: 1244-1256 Crossref PubMed Scopus (323) Google Scholar ].

Subpial delivery of adeno-associated virus 9-synapsin-caveolin-1 (AAV9-SynCav1) preserves motor neuron and neuromuscular junction morphology, motor function, delays disease onset, and extends survival in hSOD1G93A mice.

Elevating neuroprotective proteins using adeno-associated virus (AAV)-mediated gene delivery shows great promise in combating devastating neurodegenerative diseases. Amyotrophic lateral sclerosis (ALS) is one such disease resulting from loss of upper and lower motor neurons (MNs) with 90-95% of cases sporadic (SALS) in nature. Due to the unknown etiology of SALS, interventions that afford neuronal protection and preservation are urgently needed. Caveolin-1 (Cav-1), a membrane/lipid rafts (MLRs) scaffolding and neuroprotective protein, and MLR-associated signaling components are decreased in degenerating neurons in postmortem human brains. We previously showed that, when crossing our SynCav1 transgenic mouse (TG) with the mutant human superoxide dismutase 1 (hSOD1G93A) mouse model of ALS, the double transgenic mouse (SynCav1 TG/hSOD1G93A) exhibited better motor function and longer survival. The objective of the current study was to test whether neuron-targeted Cav-1 upregulation in the spinal cord using AAV9-SynCav1 could improve motor function and extend longevity in mutant humanized mouse and rat (hSOD1G93A) models of familial (F)ALS.Methods: Motor function was assessed by voluntary running wheel (RW) in mice and forelimb grip strength (GS) and motor evoked potentials (MEP) in rats. Immunofluorescence (IF) microscopy for choline acetyltransferase (ChAT) was used to assess MN morphology. Neuromuscular junctions (NMJs) were measured by bungarotoxin-a (Btx-a) and synaptophysin IF. Body weight (BW) was measured weekly, and the survival curve was determined by Kaplan-Meier analysis.Results: Following subpial gene delivery to the lumbar spinal cord, male and female hSOD1G93A mice treated with SynCav1 exhibited delayed disease onset, greater running-wheel performance, preserved spinal alpha-motor neuron morphology and NMJ integrity, and 10% increased longevity, independent of affecting expression of the mutant hSOD1G93A protein. Cervical subpial SynCav1 delivery to hSOD1G93A rats preserved forelimb GS and MEPs in the brachial and gastrocnemius muscles.Conclusion: In summary, subpial delivery of SynCav1 protects and preserves spinal motor neurons, and extends longevity in a familial mouse model of ALS without reducing the toxic monogenic component. Furthermore, subpial SynCav1 delivery preserved neuromuscular function in a rat model of FALS. The latter findings strongly indicate the therapeutic applicability of SynCav1 to treat ALS attributed to monogenic (FALS) and potentially in sporadic cases (i.e., SALS).

SAR target recognition using feature extraction of multi-scale monogenic components

SAR target recognition using feature extraction of multi-scale monogenic components

Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors

Brain tumor is considered as a fatal disease with low survival rate and has the highest cost of care per patient. This article proposes a computer-assisted system for the recognition of brain tumor image through magnetic resonance imaging based on the monogenic signal analysis. From different monogenic components, textural descriptors are obtained using completed local binary pattern and gray-level co-occurrence matrix. In the pre-processing step, various filtering for noise removal and contrast enhancement techniques are implemented. Local phase, energy and orientation components originated from the monogenic signal analysis method are used for textural feature extraction. Fisher score-based filter approach for feature selection is then employed to derive the discriminating feature set. Finally, the acquired optimal feature set is classified using the support vector machine classifier. Two benchmark MR image datasets, e-health laboratory and Harvard medical laboratory, have been used to validate the system performance. Overall detection accuracy obtained was above 99%. The experimental results demonstrate the effectiveness of the proposed approach and the potential to assist the medical experts in enhancing the detection rate. Furthermore, the presented approach delivers superior performance in brain tumor image recognition as compared to existing techniques.

SAR Target Recognition via Joint Classification of Monogenic Components with Discrimination Analysis

This paper proposes a method using joint classification of monogenic components with discrimination analysis for target recognition in synthetic aperture radar (SAR) images. Three monogenic components, namely, phase, amplitude, and orientation, are extracted from the original image and classified by joint sparse representation for target recognition. Considering that the three components may have different discrimination capabilities for different operating conditions, the discrimination analysis is incorporated into the classification scheme. The components with low discriminability are not used in the joint classification. Afterwards, those discriminative components for a certain condition are classified to determine the target type. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) to evaluate the performance of the proposed method.

SAR Target Recognition via Joint Sparse Representation of Monogenic Components with 2D Canonical Correlation Analysis

A synthetic aperture radar (SAR) target recognition approach is developed in this paper by exploiting the multiscale monogenic components, which are extracted from SAR images based on the 2D monogenic signal. The 2D canonical correlation analysis is then employed to analyze the correlations of the same monogenic components at different scales. Afterwards, the three monogenic components, i.e., local amplitude, local phase, and local orientation, at different scales are fused as three feature matrices, respectively. In order to further capture the correlations between different types of monogenic components, the joint sparse representation is used for target classification. Therefore, both the correlations of the same monogenic components at multiple scales and the relatedness among different types of monogenic components can be exploited in the proposed scheme. The real measured SAR images from the moving and stationary target acquisition and recognition dataset are classified to examine the validity of the proposal. Compared with some state-of-the-art SAR target recognition methods, the proposed approach is validated to be superior under both standard operating condition and several usual extended operating conditions according to the experimental results. In comparison with some other methods, which also use monogenic components as the basic features, the superiority of the proposed method demonstrates that it could better make use of the monogenic components to improve the classification performance.

LUIFT: LUminance Invariant Feature Transform

Illumination-invariant method for computing local feature points and descriptors, referred to as LUminance Invariant Feature Transform (LUIFT), is proposed. The method helps us to extract the most significant local features in images degraded by nonuniform illumination, geometric distortions, and heavy scene noise. The proposed method utilizes image phase information rather than intensity variations, as most of the state-of-the-art descriptors. Thus, the proposed method is robust to nonuniform illuminations and noise degradations. In this work, we first use the monogenic scale-space framework to compute the local phase, orientation, energy, and phase congruency from the image at different scales. Then, a modified Harris corner detector is applied to compute the feature points of the image using the monogenic signal components. The final descriptor is created from the histograms of oriented gradients of phase congruency. Computer simulation results show that the proposed method yields a superior feature detection and matching performance under illumination change, noise degradation, and slight geometric distortions comparing with that of the state-of-the-art descriptors.

Enhanced synthetic aperture radar automatic target recognition method based on novel features.

This paper proposes a set of uncommonly rich feature representations for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. The proposed novel feature representations capture both the spatial and spectral properties of a target in a unified framework, while simultaneously offering discrimination and robustness to aspect variations. Specifically, the proposed features are mainly derived from the ideas of the monogenic signal and polar mapping. The applicability of the monogenic signal within the field of SAR target recognition is demonstrated by its capability of capturing both the broad spectral information and spatial localization with compact support. Further, to reduce the influence of inevitable variations due to aspect changes in SAR images, the monogenic components are transformed from Cartesian to polar coordinates through polar mapping. Additionally, a new target-shadow feature is also presented to compensate for the important discriminative information about target geometry, which exists in the shadow area. Finally, the proposed features are jointly considered into a unified multiple kernel learning framework for target recognition. Experiments on the moving and stationary target acquisition and recognition (MSTAR) public dataset demonstrate the strength and applicability of the proposed representations to SAR ATR. Moreover, it is also shown that overall high recognition accuracy can be obtained by the established unified framework.

SAR Target Recognition Via Sparse Representation of Monogenic Signal on Grassmann Manifolds

In this paper, classification via sparse representation of monogenic signal on Grassmann manifolds is presented for target recognition in SAR image. To capture the broad spectral information with maximal spatial localization of SAR image, a recently proposed vector-valued analytic signal, namely monogenic signal is exploited. Different from the conventional methods, where a single feature descriptor is generated using the monogenic signal in an Euclidean space, the multiple components of monogenic signal at various scale spaces are viewed as points on a special type of Riemannian manifolds, Grassmann manifolds. The similarity between a pair of patterns (points) is measured by Grassmann distance metric. To exploit the nonlinear geometry structure further, we embed the sets of monogenic components into an implicit reproducing kernel Hilbert space (RKHS), where the kernel-based sparse signal modeling can be learnt to reach the inference. Specifically, the sets of monogenic components resulting from the training samples are concatenated first to build a redundant dictionary. Then, the counterpart of the query is efficiently approximated by superposition of atoms of the dictionary. Notably, the representation coefficients of superposition are very parsimonious. The inference is drawn by evaluating which class of training patterns could recover the query as accurately as possible. The novelty of this paper comes from 1) the development of Grassmann manifolds formed by the multiresolution monogenic signal; 2) the definition of similarity between the sets of monogenic components on Grassmann manifolds for target recognition; 3) the generalization of sparse signal modeling on Grassmann manifold; and 4) multiple comparative experiments for performance assessment.

Severe infectious diseases of childhood as monogenic inborn errors of immunity

This paper reviews the developments that have occurred in the field of human genetics of infectious diseases from the second half of the 20th century onward. In particular, it stresses and explains the importance of the recently described monogenic inborn errors of immunity underlying resistance or susceptibility to specific infections. The monogenic component of the genetic theory provides a plausible explanation for the occurrence of severe infectious diseases during primary infection. Over the last 20 y, increasing numbers of life-threatening infectious diseases striking otherwise healthy children, adolescents, and even young adults have been attributed to single-gene inborn errors of immunity. These studies were inspired by seminal but neglected findings in plant and animal infections. Infectious diseases typically manifest as sporadic traits because human genotypes often display incomplete penetrance (most genetically predisposed individuals remain healthy) and variable expressivity (different infections can be allelic at the same locus). Infectious diseases of childhood, once thought to be archetypal environmental diseases, actually may be among the most genetically determined conditions of mankind. This nascent and testable notion has interesting medical and biological implications.

Les gènes de l’obésité et leur contribution à la balance énergétique

Obesity is currently more prevalent than malnutrition, affecting about 15 % of human adult population. At population level, the Westernized environment is responsible for the outstan- ding epidemics of obesity, but at individual level genetic factors explain most of the diferences in body mass index (BMI). Indeed, obesity heritability is around 70 %. Genetic studies have identified a relatively prevalent monogenic component, due to mutations in genes controlling appetite and satiety. Genome-wide association studies (GWAS) have found more than 100 genes increasing the risk for obesity. Next-generation sequencing of obesity genes allows cost eflicient molecular diagnosis as a first step towards precision obesity medicine.

A new image retrieval model based on monogenic signal representation

This paper proposes a novel image retrieval model based on monogenic signal representation. An original image is decomposed into three complementary components: amplitude, orientation and phase by monogenic signal representation. The monogenic variation in each local region and monogenic feature in each pixel are encoded, and then the statistical features of the local features encoded are calculated. In order to overcome the problem of high feature dimensionality, the local statistical features extracted from the complementary monogenic components are projected by block-based fisher discriminant analysis, which not only reduces the dimensionality of the features extracted, but also enhances its discriminative power. Finally, these features reduced are fused for effective image retrieval. Experimental results show that our scheme can effectively describe an image, and obviously improve the average retrieval precision.

SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

Target Recognition via Information Aggregation Through Dempster–Shafer's Evidence Theory

In this letter, a novel classification via information aggregation through Dempster-Shafer's (DS) evidence theory has been presented to target recognition in a SAR image. Although the DS theory of evidence has been widely studied over the decades, less attention has been paid to its application for target recognition. To capture the characteristics of a SAR image, this letter exploits a new multidimensional analytic signal named monogenic signal. Since the components of the monogenic signal are of a high dimension, it is unrealistic to be directly used. To solve the problem, an intuitive idea is to derive a single feature by these components. However, this strategy usually results in some information loss. To boost the performance, this letter presents a classification framework via information aggregation. The monogenic components are individually fed into a recently developed algorithm, i.e., sparse representation-based classification, from which the residual with respect to each target class can be produced. Since the residual from a query sample reflects the distance to the manifold formed by the training samples of a certain class, it is reasonable to be used to define the probability mass. Then, the information provided by the monogenic signal can be aggregated via Dempster's rule; hence, the inference can be reached.

Target Recognition in SAR Images via Classification on Riemannian Manifolds

In this letter, synthetic aperture radar (SAR) target recognition via classification on Riemannian geometry is presented. To characterize SAR images, which have broad spectral information yet spatial localization, a 2-D analytic signal, i.e., the monogenic signal, is used. Then, the monogenic components are combined by computing a covariance matrix whose entries are the correlation of the components. Since the covariance matrix, a symmetric positive definite one, lies on the Riemannian manifold, it is unreasonable to be dealt with by the standard learning techniques. To address the problem, two classification schemes are proposed. The first maps the covariance matrix into the vector space and feeds the resulting descriptor into a recently developed framework, i.e., sparse representation-based classification. The other embeds the Riemannian manifold into an implicit reproducing kernel Hilbert space, followed by least square fitting technique to recover the test. The inference is reached by evaluating which class of samples could reconstruct the test as accurately as possible.

Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images

In this letter, the classification via sparse representation of the monogenic signal is presented for target recognition in SAR images. To characterize SAR images, which have broad spectral information yet spatial localization, the monogenic signal is performed. Then an augmented monogenic feature vector is generated via uniform down-sampling, normalization and concatenation of the monogenic components. The resulting feature vector is fed into a recently developed framework, i.e., sparse representation based classification (SRC). Specifically, the feature vectors of the training samples are utilized as the basis vectors to code the feature vector of the test sample as a sparse linear combination of them. The representation is obtained via l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization, and the inference is reached according to the characteristics of the representation on reconstruction. Extensive experiments on MSTAR database demonstrate that the proposed method is robust towards noise corruption, as well as configuration and depression variations.

Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

Local-feature-based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation, and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation-based local feature methods. The extensive FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-the-art local-feature-based FR methods.

Closed form of the Fourier–Borel Kernel in the Framework of Clifford Analysis

In this paper we derive for the even dimensional case a closed form of the Fourier–Borel kernel in the Clifford analysis setting. This kernel is obtained as the monogenic component in the Fischer decomposition of the exponential function \({e^{\langle \underline{x}, \underline{u} \rangle}}\) where \({\langle . , . \rangle}\) denotes the standard inner product on the m-dimensional Euclidean space. A first approach based on Clifford analysis techniques leads to a conceptual formula containing the Gamma operator and the so-called Clifford–Bessel function, two fundamental objects in the theory of Clifford analysis. To obtain an explicit expression for the Fourier–Borel kernel in terms of a finite sum of Bessel functions, this formula remains however hard to work with. To that end we have also elaborated a more direct approach based on special functions leading to recurrence formulas for a closed form of the Fourier–Borel kernel.

Robust stereo image matching using a two-dimensional monogenic wavelet transform

A robust approach based on the 2D monogenic wavelet transform (MWT), which pairs the polyharmonic B-spline wavelet basis with its complex Riesz counterparts to specify a multiresolution monogenic signal analysis, is proposed to solve the general stereo image matching problem. The disparity is directly estimated by establishing correspondences between the monogenic signal components according to the suitable local properties, i.e., the monogenic wavelet annihilates antimonogenic signals, the MWT is phase-shift covariant and the transform magnitude is phase-shift invariant. This approach is different from the current matching techniques and is promising for image registration, flow estimation, and 3D reconstruction.