Structural insights into nucleocapsid protein variability: Implications for PJ34 efficacy against SARS-CoV-2.

Human coronaviruses (HCoVs) include common cold viruses such as HCoV-229E, OC43, NL63 and HKU1 as well as MERS-CoV and SARS-CoV, which cause severe respiratory disease. Recently, SARS-CoV-2 caused a COVID-19 pandemic. The nucleocapsid (N) protein of coronaviruses, which is essential for RNA binding and homodimerization, has a highly conserved structure across viruses.

Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors.

In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e.

Comparison of perioperative outcomes of robot-assisted radical prostatectomy among the da Vinci, hinotori, and Hugo robot-assisted surgery systems.

In recent times, innovative surgical robotics have emerged and gained widespread adoption. This study aimed to compare the perioperative outcomes associated with robot-assisted radical prostatectomy (RARP) using three different robotic surgical systems: da Vinci surgical system (DVSS), hinotori surgical robot system (HSRS), and Hugo robot-assisted surgery system (HRASS). Our study involved a retrospective analysis of clinical data from 149 individuals who received RARP from 2022 to 2024, utilizing the DVSS (n = 81), HSRS (n = 52), and HRASS (n = 16).

Dielectric properties depend on the crystal structure of perovskite-type RbTaO synthesized at high pressure.

We successfully synthesized perovskite-type RbTaO at 1173 K under 4 GPa. RbTaO crystalized as a cubic system (3̄ space group (SG), = 4.04108(3) Å) at 300 K in contrast to the orthorhombic perovskite-type RbNbO prepared under the same conditions.

Utilizing convolutional neural network (CNN) for orchard irrigation decision-making.

Efficient agricultural management often relies on farmers' experiential knowledge and demands considerable labor, particularly in regions with challenging terrains. To reduce these burdens, the adoption of smart technologies has garnered increasing attention. This study proposes a convolutional neural network (CNN)-based model as a decision-support tool for smart irrigation in orchard systems, focusing on persimmon cultivation in mountainous regions.