Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads

A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk network is devised to predict full-field highly nonlinear elastic-plastic stress response for complex geometries obtained from topology optimization under variable loads. The proposed DeepONet uses a ResUNet in the trunk to encode complex input geometries, and a fully-connected branch network encodes the parametric loads. Additional information fusion is introduced via an element-wise multiplication of the encoded latent space to improve prediction accuracy further. The performance of the proposed DeepONet was compared to two baseline models, a standalone ResUNet and a DeepONet with fully connected networks as the branch and trunk. The results show that ResUNet and the proposed DeepONet share comparable accuracy; both can predict the stress field and accurately identify stress concentration points. However, the novel DeepONet is more memory efficient and allows greater flexibility with framework architecture modifications. The DeepONet with fully connected networks suffers from high prediction error due to its inability to effectively encode the complex, varying geometry. Once trained, all three networks can predict the full stress distribution orders of magnitude faster than finite element simulations. The proposed network can quickly guide preliminary optimization, designs, sensitivity analysis, uncertainty quantification, and many other nonlinear analyses that require extensive forward evaluations with variable geometries, loads, and other parameters. This work marks the first time a ResUNet is used as the trunk network in the DeepONet architecture and the first time that DeepONet solves problems with complex, varying input geometries under parametric loads and elasto-plastic material behavior

Sequential Deep Operator Networks (S-DeepONet) for Predicting Full-field Solutions Under Time-dependent Loads

Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution functions in contrast to classical neural networks that need re-training for every new set of parametric inputs. In this work, we have extended the classical formulation of DeepONets by introducing sequential learning models like the gated recurrent unit (GRU) and long short-term memory (LSTM) in the branch network to allow for accurate predictions of the solution contour plots under parametric and time-dependent loading histories. Two example problems, one on transient heat transfer and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the new architectures compared to the benchmark DeepONet model with a feed-forward neural network (FNN) in the branch. Despite being more computationally expensive, the GRU- and LSTM-DeepONets lowered the prediction error by half (0.06\% vs. 0.12\%) compared to FNN-DeepONet in the heat transfer problem, and by 2.5 times (0.85\% vs. 3\%) in the plasticity problem. In all cases, the proposed DeepONets achieved a prediction $R^2$ value of above 0.995, indicating superior accuracy. Results show that once trained, the proposed DeepONets can accurately predict the final full-field solution over the entire domain and are at least two orders of magnitude faster than direct finite element simulations, rendering it an accurate and robust surrogate model for rapid preliminary evaluations

Emerging targets in cancer drug resistance

Drug resistance is a complex phenomenon that frequently develops as a failure to chemotherapy during cancer treatment. Malignant cells increasingly generate resistance to various chemotherapeutic drugs through distinct mechanisms and pathways. Understanding the molecular mechanisms involved in drug resistance remains an important area of research for identification of precise targets and drug discovery to improve therapeutic outcomes. This review highlights the role of some recent emerging targets and pathways which play critical role in driving drug resistance

Designing impact-resistant bio-inspired low-porosity structures using neural networks

Biological structural designs in nature, like hoof walls, horns, and antlers, can be used as inspiration for generating structures with excellent mechanical properties. A common theme in these designs is the small percent porosity in the structure, ranging from 1 to 5%. In this work, the sheep horn was used as an inspiration due to its higher toughness when loaded in the radial direction compared to the longitudinal direction. Under dynamic transverse compression, we investigated the structure-property relations in low porosity structures characterized by their two-dimensional (2D) cross-sections. A diverse design space was created by combining polygonal tubules with different numbers of sides placed on a grid with varying numbers of rows and columns. The volume fraction and the orientation angle of the tubules were also varied. The finite element (FE) method was used with a rate-dependent elastoplastic material model to generate the stress-strain curves under plane-strain conditions. A gated recurrent unit (GRU) model was trained to predict the structures’ stress-strain response and energy absorption under different strain rates and applied strains. The parameter-based model uses eight discrete parameters to characterize the design space and as inputs to the model. The trained GRU model can efficiently predict the response of a new design in as little as 0.16 ms and allows rapid performance evaluation of 128,000 designs in the design space. The GRU predictions identified high-performance structures, and four design trends that affect the specific energy absorption were extracted and discussed

Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks

This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the interactions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design system to generate different lattice designs and used the finite element method to simulate their response with the Johnson-Cook material model. Using an autoencoder, we encoded the cross-sectional images of the lattices into latent design feature vectors, which were supplied to the neural network model to generate predictions. The trained models can accurately predict lattice energy absorption curves in the key-based design system and can be extended to new designs outside of the system via transfer learning

Role of prostate cancer stem-like cells in the development of antiandrogen resistance

Androgen deprivation therapy (ADT) is the standard of care treatment for advance stage prostate cancer. Treatment with ADT develops resistance in multiple ways leading to the development of castration-resistant prostate cancer (CRPC). Present research establishes that prostate cancer stem-like cells (CSCs) play a central role in the development of treatment resistance followed by disease progression. Prostate CSCs are capable of self-renewal, differentiation, and regenerating tumor heterogeneity. The stemness properties in prostate CSCs arise due to various factors such as androgen receptor mutation and variants, epigenetic and genetic modifications leading to alteration in the tumor microenvironment, changes in ATP-binding cassette (ABC) transporters, and adaptations in molecular signaling pathways. ADT reprograms prostate tumor cellular machinery leading to the expression of various stem cell markers such as Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1), Prominin 1 (PROM1/CD133), Indian blood group (CD44), SRY-Box Transcription Factor 2 (Sox2), POU Class 5 Homeobox 1(POU5F1/Oct4), Nanog and ABC transporters. These markers indicate enhanced self-renewal and stemness stimulating CRPC evolution, metastatic colonization, and resistance to antiandrogens. In this review, we discuss the role of ADT in prostate CSCs differentiation and acquisition of CRPC, their isolation, identification and characterization, as well as the factors and pathways contributing to CSCs expansion and therapeutic opportunities

Deep energy method in topology optimization applications

This paper explores the possibilities of applying physics-informed neural networks (PINNs) in topology optimization (TO) by introducing a fully self-supervised TO framework that is based on PINNs. This framework solves the forward elasticity problem by the deep energy method (DEM). Instead of training a separate neural network to update the density distribution, we leverage the fact that the compliance minimization problem is self-adjoint to express the element sensitivity directly in terms of the displacement field from the DEM model, and thus no additional neural network is needed for the inverse problem. The method of moving asymptotes is used as the optimizer for updating density distribution. The implementation of Neumann, Dirichlet, and periodic boundary conditions are described in the context of the DEM model. Three numerical examples are presented to demonstrate framework capabilities: (1) Compliance minimization in 2D under different geometries and loading, (2) Compliance minimization in 3D, and (3) Maximization of homogenized shear modulus to design 2D meta material unit cells. The results show that the optimized designs from the DEM-based framework are very comparable to those generated by the finite element method, and shed light on a new way of integrating PINN-based simulation methods into classical computational mechanics problems

Size-dependence of AM Ti–6Al–4V: Experimental characterization and applications in thin-walled structures simulations

Previous studies show that the properties of parts manufactured via additive manufacturing, such as selective laser melting, depend on local feature sizes like lattice wall thickness and strut diameter. Although size dependence has been studied extensively, it was not included in constitutive models for numerical simulations. In this work, flat dog-bone tensile specimens of different thicknesses were manufactured and tested under quasi-static conditions to characterize the size-dependent properties experimentally. It was observed that key mechanical properties decrease with specimen thickness. Through curve-fitting to experimental data, this work provides approximate analytical expressions for the material properties values as a function of specimen thickness, furnishing a phenomenological size-dependent constitutive model. The interpolating capability of the model is cross-validated with existing experimental data. Two numerical examples demonstrate the application of the size-dependent material model. The axial crushing of thin-walled lattices at varying wall thicknesses was simulated by the size-dependent material model and one that ignores size effects. Results show that ignoring size effects leads to overestimated peak crushing force and specific energy absorption. The two material models were also compared in the topology optimization of thin-walled structures. Results show that the size-dependent model leads to a more robust optimized design: having higher energy absorption and sustaining less material fracture.This is a manuscript of the article Published as He, Junyan, Shashank Kushwaha, Mahmoud A. Mahrous, Diab Abueidda, Eric Faierson, and Iwona Jasiuk. "Size-dependence of AM Ti–6Al–4V: Experimental characterization and applications in thin-walled structures simulations." Thin-Walled Structures 187 (2023): 110722. doi: https://doi.org/10.1016/j.tws.2023.110722. © 2023 by Elsevier. This manuscript is made available under the Elsevier user license (https://www.elsevier.com/open-access/userlicense/1.0/). CC BY-NC-ND

Naphthylisoindolinone alkaloids: the first ring-contracted naphthylisoquinolines, from the tropical liana Ancistrocladus abbreviatus, with cytotoxic activity

The West African liana Ancistrocladus abbreviatus is a rich source of structurally most diverse naphthylisoquinoline alkaloids. From its roots, a series of four novel representatives, named ancistrobrevolines A–D (14–17) have now been isolated, displaying an unprecedented heterocyclic ring system, where the usual isoquinoline entity is replaced by a ring-contracted isoindolinone part. Their constitutions were elucidated by 1D and 2D NMR and HR-ESI-MS. The absolute configurations at the chiral axis and at the stereogenic center were assigned by using experimental and computational electronic circular dichroism (ECD) investigations and a ruthenium-mediated oxidative degradation, respectively. For the biosynthetic origin of the isoindolinones from ‘normal’ naphthyltetrahydroisoquinolines, a hypothetic pathway is presented. It involves oxidative decarboxylation steps leading to a ring contraction by a benzilic acid rearrangement. Ancistrobrevolines A (14) and B (15) were found to display moderate cytotoxic effects (up to 72%) against MCF-7 breast and A549 lung cancer cells and to reduce the formation of spheroids (mammospheres) in the breast cancer cell line

Resistance to second generation antiandrogens in prostate cancer: pathways and mechanisms

Androgen deprivation therapy targeting the androgens/androgen receptor (AR) signaling continues to be the mainstay treatment of advanced-stage prostate cancer. The use of second-generation antiandrogens, such as abiraterone acetate and enzalutamide, has improved the survival of prostate cancer patients; however, a majority of these patients progress to castration-resistant prostate cancer (CRPC). The mechanisms of resistance to antiandrogen treatments are complex, including specific mutations, alternative splicing, and amplification of oncogenic proteins resulting in dysregulation of various signaling pathways. In this review, we focus on the major mechanisms of acquired resistance to second generation antiandrogens, including AR-dependent and AR-independent resistance mechanisms as well as other resistance mechanisms leading to CRPC emergence. Evolving knowledge of resistance mechanisms to AR targeted treatments will lead to additional research on designing more effective therapies for advanced-stage prostate cancer