Severe bone loss in the early stage of SCI was confirmed by a continuous decrease of the mineral to collagen matrix ratio. The decrease in the humeri suggested hormone level variations might participate in the etiology of SCI-induced osteoporosis.

Background. Cranial sutures are deformable joints between the bones of the skull, bridged by collagen fibres. They function to hold the bones of the skull together while allowing for mechanical stress transmission and deformation. Objective. The aim of this study is to investigate how cranial suture morphology, suture material property, and the arrangement of sutural collagen fibres influence the dynamic responses of the suture and surrounding bone under impulsive loads. Methods. An idealized bone-suture-bone complex was analyzed using a two-dimensional finite element model. A uniform impulsive loading was applied to the complex. Outcome variables of von Mises stress and strain energy were evaluated to characterize the sutures' biomechanical behavior. Results. Parametric studies revealed that the suture strain energy and the patterns of Mises stress in both the suture and surrounding bone were strongly dependent on the suture morphologies. Conclusions. It was concluded that the higher order hierarchical suture morphology, lower suture elastic modulus, and the better collagen fiber orientation must benefit the stress attenuation and energy absorption.

Applying finite-element vertical discretization to a mass-based non-hydrostatic kernel has proved difficult due to the constraints of vertical operators. This article proposes a novel hybrid finite-element vertical discretization method for a semi-implicit mass-based nonhydrostatic kernel, which integrates a finite-differential scheme and a finite-element scheme. In the hybrid method, the finite-differential scheme which satisfies the set of constraints is applied to the linear part, while a cubic finite-element scheme with high-order accuracy is applied to the non-linear part. Furthermore, to improve the accuracy of the linear part, an enlarged set of vertical levels is applied to the differential scheme. This set of vertical levels is only used to solve semi-implicit equations, and has no impact on the grid point calculation and spectral transformations. A series of 2D idealized test cases are conducted to verify the stability and the accuracy of our new method.

Most global models employ a vertical coordinate based on the moist hydrostatic pressure, and therefore do not conserve dry air mass. Such an issue should be taken seriously into account, especially in developing global high-resolution atmospheric models to address nonhydrostatic motions explicitly. In this article, we present a modified nonhydrostatic moist global spectral dynamical core using a dry-mass vertical coordinate, which conserves the mass of dry air naturally. In addition to the vertical coordinate, the modified dynamical core differs from the original Aladin-NH like dynamical kernel in the state variables employed. Specifically, a new temperature variable is introduced to formulate the governing equations and the mass continuity equation is expressed in terms of the dry air density. To assess its performance, an idealized splitting supercell test is conducted. Simulation results from both the modified and original dynamical cores are presented and compared. The results indicate that only the modified dynamic core is able to simulate the splitting supercell with good accuracy comparable to reference solutions from the Model for Prediction Across Scales (MPAS). K E Y W O R D SAladin-NH, dry-mass vertical coordinate, idealized supercell simulation, nonhydrostatic moist atmosphere, spectral dynamical core

Translocation of PKCzeta may play a central role in the development of type 2 diabetes.

Most of the present deep‐atmosphere models are based on finite‐difference schemes. This article proposes a novel deep‐atmosphere non‐hydrostatic spectral dynamical kernel with hydrostatic‐pressure based terrain‐following vertical coordinate. Contrary to finite‐difference methods, a spectral transform method is used for the horizontal discretization. The prognostic variables in our dynamical kernel are analogous to the typical ALADIN‐NH spectral model. A two time level semi‐implicit semi‐Lagrangian time‐stepping scheme is applied to enable large time steps and improve the efficiency of integration. A set of test cases are conducted to evaluate the accuracy and stability of the deep‐atmosphere dynamic kernel.

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The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning (HAVOK-ML) is developed to predict chaotic time series. HAVOK-ML simulates the time series by reconstructing a closed linear model so as to achieve the purpose of prediction. It decomposes chaotic dynamics into intermittently forced linear systems by HAVOK analysis and estimates the external intermittently forcing term using machine learning. The prediction performance evaluations confirm that the proposed method has superior forecasting skills compared with existing prediction methods.