The role of IMP dehydrogenase 2 in Inauhzin-induced ribosomal stress

The ‘ribosomal stress (RS)-p53 pathway’ is triggered by any stressor or genetic alteration that disrupts ribosomal biogenesis, and mediated by several ribosomal proteins (RPs), such as RPL11 and RPL5, which inhibit MDM2 and activate p53. Inosine monophosphate (IMP) dehydrogenase 2 (IMPDH2) is a rate-limiting enzyme in de novo guanine nucleotide biosynthesis and crucial for maintaining cellular guanine deoxy- and ribonucleotide pools needed for DNA and RNA synthesis. It is highly expressed in many malignancies. We previously showed that inhibition of IMPDH2 leads to p53 activation by causing RS. Surprisingly, our current study reveals that Inauzhin (INZ), a novel non-genotoxic p53 activator by inhibiting SIRT1, can also inhibit cellular IMPDH2 activity, and reduce the levels of cellular GTP and GTP-binding nucleostemin that is essential for rRNA processing. Consequently, INZ induces RS and the RPL11/RPL5-MDM2 interaction, activating p53. These results support the new notion that INZ suppresses cancer cell growth by dually targeting SIRT1 and IMPDH2

MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT

Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques in computed tomography (CT). Traditional IQA methods relying on hand-crafted features have limitations in summarizing the subjective perceptual experience of image quality. Recent deep learning-based approaches have demonstrated strong modeling capabilities and potential for medical IQA, but challenges remain regarding model generalization and perceptual accuracy. In this work, we propose a multi-scale distributions regression approach to predict quality scores by constraining the output distribution, thereby improving model generalization. Furthermore, we design a dual-branch alignment network to enhance feature extraction capabilities. Additionally, semi-supervised learning is introduced by utilizing pseudo-labels for unlabeled data to guide model training. Extensive qualitative experiments demonstrate the effectiveness of our proposed method for advancing the state-of-the-art in deep learning-based medical IQA. Code is available at: https://github.com/zunzhumu/MD-IQA

Facile purification of colloidal NIR-responsive gold nanorods using ions assisted self-assembly

Anisotropic metal nanoparticles have been paid much attention because the broken symmetry of these nanoparticles often leads to novel properties. Anisotropic gold nanoparticles obtained by wet chemical methods inevitably accompany spherical ones due to the intrinsically high symmetry of face-centred cubic metal. Therefore, it is essential for the purification of anisotropic gold nanoparticles. This work presents a facile, low cost while effective solution to the challenging issue of high-purity separation of seed-mediated grown NIR-responsive gold nanorods from co-produced spherical and cubic nanoparticles in solution. The key point of our strategy lies in different shape-dependent solution stability between anisotropic nanoparticles and symmetric ones and selective self-assembly and subsequent precipitation can be induced by introducing ions to the as-made nanorod solution. As a result, gold nanorods of excellent purity (97% in number density) have been obtained within a short time, which has been confirmed by SEM observation and UV-vis-NIR spectroscopy respectively. Based on the experimental facts, a possible shape separation mechanism was also proposed

Coherent interface strengthening of ultrahigh pressure heat-treated Mg-Li-Y alloys.

Achieving good strength-ductility of Mg alloys has always been a crucial issue for the widespread applications of Mg-based structural materials. Herein, an unexpected double-stage strengthening phenomenon was discovered in Mg-8Li-1Y(wt.%) alloys through high pressure (6 GPa) heat treatments over a range of 700-1300°C. Attractively, the yield strength values are improved remarkably without losing their ductility. The low temperature strengthening mechanism is mainly driven by the formation of large-volume nanoscale contraction twins. In contrast, the high-temperature strengthening reason is ascribed to the presence of densely nano-sized stacking faults. Both coherent interfaces contribute effectively to high mechanical strength without any tradeoff in ductility

Exploring the ex-situ components within Gaia DR3

The presence of Gaia DR3 provides a large sample of stars with complete 6D information, offering a fertile ground for the exploration of stellar objects that were accreted to the Milky Way through ancient merger events. In this study, we developed a deep learning methodology to identify ex-situ stars within the Gaia DR3 catalogue. After two phases of training, our neural network (NN) model was capable of performing binary classification of stars based on input data consisting of 3D position and velocity, as well as actions. From the target sample of 27 085 748 stars, our NN model managed to identify 160 146 ex-situ stars. The metallicity distribution suggests that this ex-situ sample comprises multiple components but appears to be predominated by the Gaia-Sausage-Enceladus. We identified member stars of the Magellanic Clouds, Sagittarius, and 20 globular clusters throughout our examination. Furthermore, an extensive group of member stars from Gaia-Sausage-Enceladus, Thamnos, Sequoia, Helmi streams, Wukong, and Pontus were meticulously selected, constituting an ideal sample for the comprehensive study of substructures. Finally, we conducted a preliminary estimation to determine the proportions of ex-situ stars in the thin disc, thick disc, and halo, which resulted in percentages of 0.1%, 1.6%, and 63.2%, respectively. As the vertical height from the Galactic disc and distance from the Galactic centre increased, there was a corresponding upward trend in the ex-situ fraction of the target sample

The Large High Altitude Air Shower Observatory (LHAASO) Science White Paper

The Large High Altitude Air Shower Observatory (LHAASO) project is a new generation multi-component instrument, to be built at 4410 meters of altitude in the Sichuan province of China, with the aim to study with unprecedented sensitivity the spec trum, the composition and the anisotropy of cosmic rays in the energy range between 10$^{12}$ and 10$^{18}$ eV, as well as to act simultaneously as a wide aperture (one stereoradiant), continuously-operated gamma ray telescope in the energy range between 10$^{11}$ and $10^{15}$ eV. The experiment will be able of continuously surveying the TeV sky for steady and transient sources from 100 GeV to 1 PeV, t hus opening for the first time the 100-1000 TeV range to the direct observations of the high energy cosmic ray sources. In addition, the different observables (electronic, muonic and Cherenkov/fluorescence components) that will be measured in LHAASO will allow to investigate origin, acceleration and propagation of the radiation through a measurement of energy spec trum, elemental composition and anisotropy with unprecedented resolution. The remarkable sensitivity of LHAASO in cosmic rays physics and gamma astronomy would play a key-role in the comprehensive general program to explore the High Energy Universe. LHAASO will allow important studies of fundamental physics (such as indirect dark matter search, Lorentz invariance violation, quantum gravity) and solar and heliospheric physics. In this document we introduce the concept of LHAASO and the main science goals, providing an overview of the project.Comment: This document is a collaborative effort, 185 pages, 110 figure

Highly-dispersed nickel nanoparticles decorated titanium dioxide nanotube array for enhanced solar light absorption

Honeycomb titanium dioxide nanotube array (TiO2-NTA) decorated by highly-dispersed nickel nanoparticles (Ni-NPs) has been grown under control on Ti foil by anodization and subsequent electrodeposition. The pore diameter and length of TiO2-NTA, and the size and quantity of Ni-NPs can be controlled via modulating the variables of the electrochemical processes. It has been found that the pretreatment of TiO2-NTA in the Cu(NO3)2 solution and further annealing at 450 °C in air could greatly improve the dispersion of the electrodeposited Ni-NPs. Absorption of the light in the solar spectrum from 300 to 2500 nm by the Ni-NPs@TiO2-NTA is as high as 96.83%, thanks to the co-effect of the light-trapping of TiO2-NTA and the plasmonic resonance of Ni-NPs. In the water heating experiment performed under an illuminating solar power density of ∼1 kW m−2 (AM 1.5), the ultimate temperature over 66 °C and an overall efficiency of 78.9% within 30 min were obtained, promising for applications in photothermal conversion and solar energy harvest

Dynamics of a delayed diffusive predator-prey model with Allee effect and nonlocal competition in prey and hunting cooperation in predator

&lt;abstract&gt;&lt;p&gt;In this paper, a delayed diffusive predator-prey model with the Allee effect and nonlocal competition in prey and hunting cooperation in predators is proposed. The local stability of coexisting equilibrium and the existence of Hopf bifurcation are studied by analyzing the eigenvalue spectrum. The property of Hopf bifurcation is also studied by the center manifold theorem and normal form method. Through numerical simulation, the analysis results are verified, and the influence of these parameters on the model is also obtained. Firstly, increasing the Allee effect parameter $\beta$ and hunting cooperation parameter $\alpha$ is not conducive to the stability of the coexistence equilibrium point under some parameters. Secondly, the time delay can also affect the stability of coexisting equilibrium and induce periodic solutions. Thirdly, the nonlocal competition in prey can affect the dynamic properties of the predator-prey model and induce new dynamic phenomena (stably spatially inhomogeneous bifurcating periodic solutions).&lt;/p&gt;&lt;/abstract&gt;</jats:p