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    Grade Group 1 Prostate Cancers Exhibit Tumor-defining Androgen Receptor–driven Programs

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    Grade group 1 (GG1) primary prostate cancers with a pathologic Gleason score of 6 are considered indolent and generally not associated with fatal outcomes, so treatment is not indicated for most cases. These low-grade cancers have an overall negligible risk of locoregional progression and metastasis to distant organs, which is why there is an ongoing debate about whether these lesions should be reclassified as “noncancerous”. However, the underlying molecular activity of key disease drivers, such as the androgen receptor (AR), have thus far not been thoroughly characterized in low-grade tumors. Therefore, we set out to delineate the AR chromatin-binding landscape in low-grade GG1 prostate cancers to gain insights into whether these AR-driven programs are actually tumor-specific or are normal prostate epithelium-like. These analyses showed that GG1 tumors do not harbor a distinct AR cistrome and, similar to higher-grade cancers, AR preferentially binds to tumor-defining cis-regulatory elements. Furthermore, the enhancer activity of these regions and the expression of their respective target genes were not significantly different in GG1 tumors. From an epigenetic perspective, this finding supports the cancer designation currently given to these low-grade tumors and clearly distinguishes them from noncancerous benign tissue. Patient summary: We characterized the molecular activity of the androgen receptor protein, which drives prostate cancer disease, in low-grade tumors. Our results show that these tumors are true cancers and are clearly separate from benign prostate tissue despite their low clinical aggressiveness.</p

    Engineering buffer layers to improve temperature resilience of magnetic tunnel junction sensors

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    Improving the thermal resilience of magnetic tunnel junctions (MTJs) broadens their applicability as sensing devices and is necessary to ensure their operation under harsh environments. In this work, we are address the impact of temperature on the degradation of the magnetic reference in field sensor stacks based on MgO-MTJs. Our study starts by simple MnIr/CoFe bilayers to gather enough insights into the role of critical morphological and magnetic parameters and their impact in the temperature dependent behavior. The exchange bias coupling field (Hex), coercive field (Hc), and blocking temperature (Tb) distribution are tuned, combining tailored growth conditions of the antiferromagnet and different buffer layer materials and stackings. This is achieved by a unique combination of ion beam deposition and magnetron sputtering, without vaccum break. Then, the work then extends beyond bilayers into more complex state-of-the-art MgO MTJ stacks as those employed in commercial sensing applications. We systematically address their characteristic fields, such as the width of the antiferromagnetic coupling plateau ΔH, and study their dependence on temperature. Although, [Ta/CuN] buffers showed higher key performance indications (e.g.Hex) at room temperature in both bilayers and MTJs, [Ta/Ru] buffers showed an overall wider ΔHup to 200 °C, more suitable to push high temperature operations. This result highlights the importance of properly design a suitable buffer layer system and addressing the complete MTJ behavior as function of temperature, to deliver the best stacking design with highest resilience to high temperature environments.</p

    Knight, David

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    van den Boogaard, Maartje

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    Surrogate modeling in irreversible electroporation towards real-time treatment planning

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    In this paper, we develop surrogate models that can replace expensive predictive models and account for uncertainties in real-time treatment planning for irreversible electroporation of liver tumors. Standard non-intrusive surrogate modeling techniques that account for the model uncertainty and reduce the computational cost, such as polynomial chaos expansion and Gaussian process regression with conventional kernels, often do not capture the true physical behavior of the treatment outcome as required in the context of treatment planning. We improve the Gaussian process regression model by modifying the kernel function to a non-stationary Gibbs kernel with a support vector machine-based classifier in its length scale definition. This proposed model is compared with the standard surrogates in terms of their performance and accuracy. Our model is able to accurately replicate the behavior of the biophysics-based predictive model. There is a decrease of at least 81% in the overall root-mean-square error for treatment outcome when compared to the Gaussian process regression model with conventional kernels. Furthermore, we illustrate the application of the proposed surrogate model in treatment planning to address a voltage optimization problem for complete tumor ablation. Surrogate-assisted treatment planning exhibited good performance while maintaining similar levels of accuracy in comparison to treatment planning based on biophysical models. Finally, the effect of uncertainty in tissue electrical conductivities on the optimal voltage value is discussed.</p

    On the impact of electric field fluctuations on microtearing turbulence

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    The magnetic drift and the electric potential play an important role in microtearing destabilization by increasing the growth rate of this instability in the presence of collisions, while in electrostatic plasma micro-turbulence, zonal electric potentials can have a strong impact on turbulent saturation. A reduced model has been developed, showing that the Rechester-Rosenbluth model is a good model for the prediction of electron heat diffusivity by microtearing turbulence. Here, nonlinear gyrokinetic flux-tube simulations are performed in order to compute the characteristics of microtearing turbulence and the associated heat fluxes in tokamak plasmas and to assess how zonal flows and zonal fields affect saturation. This is consistent with a change in saturation mechanism from temperature corrugations to zonal field- and zonal flow-based energy transfer. It is found that removing the electrostatic potential causes a flux increase, while linearly stabilization is observed.</p

    Parametric Continuous-Time Blind System Identification

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    In this paper, the blind system identification problem for continuous-time systems is considered. A direct continuous-time estimator is proposed by utilising a state-variable-filter least squares approach. In the proposed method, coupled terms between the numerator polynomial of the system and input parameters appear in the parameter vector which are subsequently separated using a rank-1 approximation. An algorithm is then provided for the direct identification of a single-input single-output linear time-invariant continuous-time system which is shown to satisfy the property of correctness under some mild conditions. Monte Carlo simulations demonstrate the performance of the algorithm and verify that a model and input signal can be estimated to a proportion of their true values

    Variationally mimetic operator networks

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    In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions, and boundary data to the solution of a PDE. This work describes a new architecture for operator networks, called the variationally mimetic operator network (VarMiON ), that mimics the form of the numerical solution obtained from an approximate variational or weak formulation of the problem. Like the conventional Deep Operator Network (DeepONet) the VarMiON is also composed of a sub-network that constructs the basis functions for the output and another that constructs the coefficients for these basis functions. However, in contrast to the DeepONet, the architecture of these sub-networks in the VarMiON is precisely determined. An analysis of the error in the VarMiON solution reveals that it contains contributions from the error in the training data, the training error, the quadrature error in sampling input and output functions, and a “covering error” that measures the distance between the test input functions and the nearest functions in the training dataset. It also depends on the stability constants for the exact solution operator and its VarMiON approximation. The application of the VarMiON to a canonical elliptic PDE and a nonlinear PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet and a recently proposed multiple-input operator network (MIONet). Further, its performance is more robust to variations in input functions, the techniques used to sample the input and output functions, the techniques used to construct the basis functions, and the number of input functions. Moreover, it consistently outperforms baseline methods at various dataset sizes. The data and code accompanying this manuscript are publicly available at https://github.com/dhruvpatel108/VarMiON.</p

    Image-guided subject-specific modeling of glymphatic transport and amyloid deposition

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    The glymphatic system is a brain-wide system of perivascular networks that facilitate exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) to remove waste products from the brain. A greater understanding of the mechanisms for glymphatic transport may provide insight into how amyloid beta (Aβ) and tau agglomerates, key biomarkers for Alzheimer's disease and other neurodegenerative diseases, accumulate and drive disease progression. In this study, we develop an image-guided computational model to describe glymphatic transport and Aβ deposition throughout the brain. Aβ transport and deposition are modeled using an advection–diffusion equation coupled with an irreversible amyloid accumulation (damage) model. We use immersed isogeometric analysis, stabilized using the streamline upwind Petrov–Galerkin (SUPG) method, where the transport model is constructed using parameters inferred from brain imaging data resulting in a subject-specific model that accounts for anatomical geometry and heterogeneous material properties. Both short-term (30-min) and long-term (12-month) 3D simulations of soluble amyloid transport within a mouse brain model were constructed from diffusion weighted magnetic resonance imaging (DW-MRI) data. In addition to matching short-term patterns of tracer deposition, we found that transport parameters such as CSF flow velocity play a large role in amyloid plaque deposition. The computational tools developed in this work will facilitate investigation of various hypotheses related to glymphatic transport and fundamentally advance our understanding of its role in neurodegeneration, which is crucial for the development of preventive and therapeutic interventions.</p


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