Nievesnixon7334

An effective and reliable Finnis-Sinclair (FS) type potential is developed for large-scale molecular dynamics (MD) simulations of plasticity and phase transition of magnesium (Mg) single crystals under high-pressure shock loading. The shock-wave profiles exhibit a split elastic-inelastic wave in the [0001]HCPshock orientation and a three-wave structure in the [10-10]HCPand [-12-10]HCPdirections, namely, an elastic precursor, a followed plastic front, and a phase-transition front. The shock Hugoniot of the particle velocity (Up) vs the shock velocity (Us) of Mg single crystals in three shock directions under low shock strength reveals apparent anisotropy, which vanishes with increasing shock strength. For the [0001]HCPshock direction, the amorphization caused by strong atomic strain plays an important role in the phase transition and allows for the phase transition from an isotropic stressed state to the product phase. The reorientation in the shock directions [10-10]HCPand [-12-10]HCP, as the primary plasticiinstability in the elastic precursor, and the plasticity or the phase transition relaxed the shear stress.We investigated the effects of indirect apoptotic cell death due to vascular damage on tumor response to a single large dose with an improved two-dimensional cellular automata model. The tumor growth was simulated by considering the oxygen and nutrients supplied to the tumor through the blood vessels. The cell damage processes were modeled by taking account of the direct cell death and the indirect death due to the radiation-induced vascular damages. The radiation increased the permeation of oxygen and nutrients through the blood vessel or caused the breakdown of the vasculature. The amount of oxygen in cancer cells affected the response of cancer cells to radiation and the tumor growth rate after irradiation. The lack of oxygen led to the apoptotic death of cancer cells. We calculated the tumor control probability (TCP) at different radiation doses, the probability of apoptotic death, the threshold of the oxygen level for indirect apoptotic death, the average oxygen level in cancer cells and the vessel survival probability after radiation damage. Due to the vessel damage, indirect cell death led to a 4% increase in TCP for the dose ranging from 15 Gy to 20 Gy. TCP increased with increasing the probability of apoptotic death and the threshold of the oxygen level for indirect apoptotic death due to increased apoptotic death. The variation of TCP as a function of the average oxygen level exhibited the minimum at the average oxygen level of 2.7%. The apoptosis increased as the average oxygen level decreased, leading to an increasing TCP. On the other hand, the direct radiation damage increased, and the apoptosis decreased for higher average oxygen level, resulting in a higher TCP. We showed by modeling the radiation damage of blood vessels in a 2D CA simulation that the indirect apoptotic death of cancer cells, caused by the reduction of the oxygen level due to vascular damage after high dose irradiation, increased TCP.Objective.Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. DZD9008 molecular weight Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing.Approach.We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data.Main results.The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a99.38%accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of99.46%.Significance.The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.Calculating the electronic structure of systems involving very different length scales presents a challenge. Empirical atomistic descriptions such as pseudopotentials or tight-binding models allow one to calculate the effects of atomic placements, but the computational burden increases rapidly with the size of the system, limiting the ability to treat weakly bound extended electronic states. Here we propose a new method to connect atomistic and quasi-continuous models, thus speeding up tight-binding calculations for large systems. We divide a structure into blocks consisting of several unit cells which we diagonalize individually. We then construct a tight-binding Hamiltonian for the full structure using a truncated basis for the blocks, ignoring states having large energy eigenvalues and retaining states with energies close to the band edge energies. A numerical test using a GaAs/AlAs quantum well shows the computation time can be decreased to less than 5% of the full calculation with errors of less than 1%. We give data for the trade-offs between computing time and loss of accuracy. We also tested calculations of the density of states for a GaAs/AlAs quantum well and find a ten times speedup without much loss in accuracy.Extension of the topological concepts to the bosonic systems has led to the prediction of topological phonons in materials. Here we discuss the topological phonons and electronic structure of Li2BaX (X = Si, Ge, Sn, and Pb) materials using first-principles theoretical modelling. A careful analysis of the phonon spectrum of Li2BaX reveals an optical mode inversion with the formation of nodal line states in the Brillouin zone. Our electronic structure results reveal a double band inversion at the Γ point with the formation of inner nodal-chain states in the absence of spin-orbit coupling (SOC). Inclusion of the SOC opens a materials-dependent gap at the band crossing points and transitions the system into a trivial insulator state. We also discuss the lattice thermal conductivity and transport properties of Li2BaX materials. Our results show that coexisting phonon and electron nontrivial topology with robust transport properties would make Li2BaX materials appealing for device applications.We are interested in learning the hyperparameters in a convex objective function in a supervised setting. The complex relationship between the input data to the convex problem and the desirable hyperparameters can be modeled by a neural network; the hyperparameters and the data then drive the convex minimization problem, whose solution is then compared to training labels. In our previous work (Xu and Noo 2021Phys. Med. Biol.6619NT01), we evaluated a prototype of this learning strategy in an optimization-based sinogram smoothing plus FBP reconstruction framework. A question arising in this setting is how to efficiently compute (backpropagate) the gradient from the solution of the optimization problem, to the hyperparameters to enable end-to-end training. In this work, we first develop general formulas for gradient backpropagation for a subset of convex problems, namely the proximal mapping. To illustrate the value of the general formulas and to demonstrate how to use them, we consider the specific instance of 1D quadratic smoothing (denoising) whose solution admits a dynamic programming (DP) algorithm. The general formulas lead to another DP algorithm for exact computation of the gradient of the hyperparameters. Our numerical studies demonstrate a 55%-65% computation time savings by providing a custom gradient instead of relying on automatic differentiation in deep learning libraries. While our discussion focuses on 1D quadratic smoothing, our initial results (not presented) support the statement that the general formulas and the computational strategy apply equally well to TV or Huber smoothing problems on simple graphs whose solutions can be computed exactly via DP.

Whether combined radiation and tyrosine kinase inhibitor (TKI) therapy in non-small cell lung cancer (NSCLC) patients with brain metastases (BMs) and epidermal growth factor receptor (EGFR) mutations confers additional benefits over TKI therapy alone remains a matter of debate. The goal of this study was to compare outcomes between combined TKI therapy with stereotactic radiosurgery (SRS) versus TKI therapy alone in NSCLC patients with BMs and EGFR mutations.

Consecutive cases of NSCLC patients with EGFR mutations and BMs treated with TKIs were selected for inclusion in this study. Patients were categorized into two groups based on SRS TKI therapy alone (group I) and combined SRS and TKI therapy (group II). Patients who had SRS or TKI as salvage therapy and those with prior radiation treatment for BMs were excluded. Tumor control (< 10% increase in tumor volume) and overall survival (OS) rates were compared using Kaplan-Meier analyses. Independent predictors of tumor control and OS were identified usinTKI therapy is recommended for intracranial disease control in NSCLC patients with BMs and EGFR mutations. Potential benefits may include prevention of neurological deficits and seizures. Future prospective studies may help clarify the clinical outcome benefits of SRS in these patients.

Although the OS rate did not differ between TKI therapy with and without SRS, the addition of SRS to TKI therapy resulted in improvement of intracranial tumor control. The lack of effect on survival rate with the addition of SRS may be attributable to extracranial disease progression. The addition of SRS to TKI therapy is recommended for intracranial disease control in NSCLC patients with BMs and EGFR mutations. Potential benefits may include prevention of neurological deficits and seizures. Future prospective studies may help clarify the clinical outcome benefits of SRS in these patients.

Epilepsy surgery for older adults is controversial owing to their longer duration of epilepsy and perceived higher surgical risk. However, because of an aging population and documented benefit of epilepsy surgery, surgery is considered more frequently for these patients. The authors' objective was to analyze the role of resective surgery in patients older than 60 years and to assess outcomes and safety.

The authors conducted a retrospective analysis of 595 patients who underwent resective epilepsy surgery at their center from 1999 to 2018. Thirty-one patients aged 60 years or older were identified. Sixty patients younger than 60 years were randomly selected as controls. Population characteristics, results of presurgical evaluations, outcomes, and complications were analyzed.

No significant differences were found between the groups in terms of hemisphere dominance, side of surgery, presence of a lesion, and incidence of temporal lobe epilepsy. Epilepsy duration was greater in the older cohort (p = 0.019), and invasive EEG was more commonly employed in younger patients (p = 0.