1,131 publications from this institution
<p>MCC Patient characteristics (S1); Extension cohort of MCCs (S2); List of genes represented on the targeted sequencing panel (S3); Performance metrics for targeted sequencing and low coverage WGS (S4); Validation of mutations by orthogonal genotyping methods (S5); Immunohistochemistry scores for MCCs (S6); SNV and Indels called from targeted sequencing (S7); Mutational burden with respect to copy number changes through low coverage whole genome sequencing (S8); Gene level copy-number from LC-WGS (S9); Copies and viral integration sites for MCV positive MCCs (S10); Drug sensitivity data for 3 MCC cell lines (S11).</p>
• Physics-informed neural networks for elastoplasticity in strong and weak forms. • Strong form supports forward prediction and parameter inversion with data loss. • Data-free weak form captures plastic evolution via energy minimization. • Kolmogorov–Arnold network is compared with multilayer perceptron in both forms. Physics-informed neural networks have recently achieved remarkable success in solving elastic problems by embedding governing equations into the training of neural networks. Building upon these advances, this study extends physics-informed neural networks to material nonlinearity and develops two computational frameworks for small-strain von Mises elastoplasticity. The strong-form framework enforces governing equations through pointwise residual minimization, enabling unified forward–inverse modeling of field variables and unknown material parameters. In contrast, the weak-form framework derived from total potential energy minimization allows data-free learning of elastoplastic evolution through incremental loading, yielding stable and physically consistent predictions. The recently developed Kolmogorov–Arnold network is further incorporated and compared with the conventional multilayer perceptron. Results show that the Kolmogorov–Arnold network alleviates the gradient oscillation and convergence instability in the weak-form framework but performs less effectively in the strong form. Furthermore, validations against reference solutions from conventional numerical methods demonstrate the potential of the developed physics-informed neural network frameworks as mesh-free surrogates for elastoplastic analysis.
This study is aimed to investigate the role of long non-coding RNA 630 (LINC00630) in hepatocellular carcinoma (HCC). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to examine LINC00630 expression in HCC cell lines and tissues. After LINC00630 was overexpressed or depleted in HCC cell lines, cell counting kit-8 (CCK-8) assay, BrdU assay, and flow cytometry were conducted for detecting HCC cell multiplication, apoptosis, and cell cycle progression. The catRAPID database was adopted to predict the binding relationship between LINC00630 and E2F transcription factor 1 (E2F1), and RNA pull-down and RNA immunoprecipitation (RIP) assays were carried out to verify this binding relationship. The binding of E2F1 to the cyclin-dependent kinase 2 (CDK2) promoter region was verified by dual-luciferase reporter gene assay and chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR) assay. Western blotting was conducted to detect the protein expression of E2F1 and CDK2 in HCC cells. We report that LINC00630 expression was up-regulated in HCC and was significantly correlated with TNM stage and lymph node metastasis. LINC00630 overexpression facilitated HCC cell proliferation and cell cycle progression and inhibited the cell apoptosis, while LINC00630 knockdown had the opposite effects. LINC00630 directly bounds with E2F1. LINC00630 overexpression enhanced the binding of E2F1 to the CDK2 promoter region, thereby promoting CDK2 transcription, whereas knocking down LINC00630 inhibited CDK2 transcription. Collectively, LINC00630 promoted CDK2 transcription by recruiting E2F1 to the promoter region of CDK2, thereby promoting the malignant progression of HCC. Our data suggest that LINC00630 is a promising molecular target for HCC.
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Treatment options for patients with advanced prostate cancer (PCa) remain limited. Improved understanding of the underlying molecular drivers of prostate cancer pathogenesis, progression and resistance development has provided the fundamental basis for rational targeted drug design.This review will discuss the most recent developments in the field of prostate cancer therapies including key findings such as the identification of ETS gene rearrangements, the dissection of prostate cancer molecular heterogeneity and the discovery that castration-resistant prostate cancer (CRPC) remains androgen-driven despite the androgen-depleted milieu, thus making androgen receptor signaling a continued focus of molecularly targeted treatments. A multitude of new molecularly targeted agents are in clinical development and are highly likely to change the current treatment paradigm.This review will outline the current clinical development of molecular targeted treatments in CRPC.Unraveling the complex molecular biology that underpins this heterogeneous disease may pave the way to personalized therapy with a wide range of rationally targeted agents and combination treatments. In conclusion, we can predict that the rational clinical development of new targeted drugs will improve the outcome of men with prostate cancer in the years ahead.
