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Xiaolin Hu

Publications and source records attributed to Xiaolin Hu.

2 recordsLinked to original sources

Uncovering phenotypic expansion in AXIN2-related disorders through precision animal modeling.

PURPOSE: Heterozygous pathogenic variants in AXIN2 (HGNC: 904) cause oligodontia-colorectal cancer syndrome. We identified 5 individuals with de novo heterozygous variants [NM_004655.4:c.196G>A p.(Glu66Lys), c.197A>G p.(Glu66Gly), and c.199G>A p.(Gly67Arg)] in AXIN2. Common phenotypes among these individuals included ectodermal dysplasia, global developmental delay, microcephaly, and limb, ophthalmologic, and genitourinary abnormalities. METHODS: Structural modeling was performed to predict the impact of these variants on AXIN2. A prime editing N1 screen of mouse embryos was performed to test whether the p.Glu66Lys variant produces a phenotype. Drosophila models were used to test the effect of this variant on Wnt signaling. RESULTS: Structural modeling suggests that these variants disrupt AXIN2 binding to tankyrase, which regulates AXIN2 levels through poly-ADP-ribosylation. Heterozygous (p.Glu66Lys) mouse embryos were perinatally lethal with soft palate clefts and skeletal abnormalities. Modeling of the p.Glu66Lys variant in the Drosophila wing suggests gain-of-function or dominant-negative activity compared to reference AXIN2. CONCLUSION: Specific variants in the tankyrase-binding domain of AXIN2 are pathogenic, leading to phenotypic expansion with potential context-dependent effects on AXIN2 function and Wnt signaling. The N1 modeling strategy used to demonstrate variant pathogenicity may be beneficial for resolving other heterozygous variants associated with congenital anomalies.

AXIN2↗

Solving pseudomonotone variational inequalities and pseudoconvex optimization problems using the projection neural network.

In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.

Algorithms↗