Tremendous progress has been made in the field of ferroptosis since this regulated cell death process was first named in 2012. Ferroptosis is initiated upon redox imbalance and driven by excessive phospholipid peroxidation. Levels of multiple intracellular nutrients (iron, selenium, vitamin E and coenzyme Q10) are intimately related to the cellular antioxidant system and participate in the regulation of ferroptosis. Dietary intake of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) regulates ferroptosis by directly modifying the fatty acid composition in cell membranes. In addition, amino acids and glucose (energy stress) manipulate the ferroptosis pathway through the nutrient-sensitive kinases mechanistic target of rapamycin complex 1 (mTORC1) and AMP-activated protein kinase (AMPK). Understanding the molecular interaction between nutrient signals and ferroptosis sensors might help in the identification of the roles of ferroptosis in normal physiology and in the development of novel pharmacological targets for the treatment of ferroptosis-related diseases.

Based on an accurate numerical solution of the kinetic equation using well-resolved spatial and velocity grids, the separation of rarefied gas flow in a microchannel with double rectangular bends is investigated over a wide range of Knudsen and Reynolds numbers. Rarefaction effects are found to play different roles in flow separation (vortex formation) at the concave and convex corners. Flow separations near the concave and convex corners are only observed for a Knudsen number up to $0.04$ and $0.01$, respectively. With further increase of the Knudsen number, flow separation disappears. Due to the velocity slip at the solid walls, the concave (convex) vortex is suppressed (enhanced), which leads to the late (early) onset of separation of rarefied gas flows with respect to the Reynolds number. The critical Reynolds numbers for the emergence of concave and convex vortices are found to be as low as $0.32\times 10^{-3}$ and $30.8$, respectively. The slip velocity near the concave (convex) corner is found to increase (decrease) when the Knudsen number increases. An adverse pressure gradient appears near the concave corner for all the examined Knudsen numbers, while for the convex corner it only occurs when the Knudsen number is less than $0.1$. Due to the secondary flow and adverse pressure gradient near the rectangular bends, the mass flow rate ratio between the bent and straight channels of the same length is a non-monotonic function of the Knudsen number. Our results clarify the diversified and often contradictory observations reported in the literature about flow rate enhancement and vortex formation in bent microchannels.

The mammary gland, a unique exocrine organ, is responsible for milk synthesis in mammals. Neonatal growth and health are predominantly determined by quality and quantity of milk production. Amino acids are crucial maternal nutrients that are the building blocks for milk protein and are potential energy sources for neonates. Recent advances made regarding the mammary gland further demonstrate that some functional amino acids also regulate milk protein and fat synthesis through distinct intracellular and extracellular pathways. In the present study, we discuss recent advances in the role of amino acids (especially branched-chain amino acids, methionine, arginine and lysine) in the regulation of milk synthesis. The present review also addresses the crucial questions of how amino acids are transported, sensed and transduced in the mammary gland.

The kernel extreme learning machine (KELM) is more robust and has a faster learning speed when compared with the traditional neural networks, and thus it is increasingly gaining attention in hyperspectral image (HSI) classification. Although the Gaussian radial basis function kernel widely used in KELM has achieved promising classification performance in supervised HSI classification, it does not consider the underlying data structure of HSIs. In this paper, we propose a novel spectral-spatial KELM method (termed as MF-KELM) by incorporating the mean filtering kernel into the KELM model, which can properly compute the mean value of the spatial neighboring pixels in the kernel space. Considering that in the situation of limited training samples the classification result is very noisy, the spatial bilateral filtering information on spectral band-subsets is introduced to improve the accuracy. Experiment results show that our method outperforms other kernel functions based on KELM in terms of classification accuracy and visual comparison.

Numerical simulations of unsteady gas flows are studied on the basis of Gas-Kinetic Unified Algorithm (GKUA) from rarefied transition to continuum flow regimes. Several typical examples are adopted. An unsteady flow solver is developed by solving the Boltzmann model equations, including the Shakhov model and the Rykov model etc. The Rykov kinetic equation involving the effect of rotational energy can be transformed into two kinetic governing equations with inelastic and elastic collisions by integrating the molecular velocity distribution function with the weight factor on the energy of rotational motion. Then, the reduced velocity distribution functions are devised to further simplify the governing equation for one- and two-dimensional flows. The simultaneous equations are numerically solved by the discrete velocity ordinate (DVO) method in velocity space and the finite-difference schemes in physical space. The time-explicit operator-splitting scheme is constructed, and numerical stability conditions to ascertain the time step are discussed. As the application of the newly developed GKUA, several unsteady varying processes of one- and two-dimensional flows with different Knudsen number are simulated, and the unsteady transport phenomena and rarefied effects are revealed and analyzed. It is validated that the GKUA solver is competent for simulations of unsteady gas dynamics covering various flow regimes.