Depression imposes a staggering global socioeconomic burden. Current pharmacotherapies face major limitations, including slow efficacy, adverse effects, and non-response rates of up to 55%, necessitating novel therapeutic modalities. This study introduces terahertz (THz) photoneuromodulation as an innovative physical intervention for depression, offering several advantages over conventional pharmacological or optogenetic approaches. Mild THz photoneuromodulation circumvents the need for exogenous agents or genetic modifications, mitigating potential risks while precisely modulating neurotransmitter levels and neuronal excitability to alleviate depression-like behaviors. In a chronic restraint stress (CRS) mouse model, THz photostimulation rapidly attenuated hyperactivity and increased serotonin levels by 107.5% ± 45.3% in lateral orbitofrontal cortex glutamatergic neurons (OFC^Glu) compared to those treated with antidepressants. This led to marked improvements in depressive-like behaviors and cognitive function. Furthermore, THz modulation of OFC activity recapitulated the effects of chemogenetic inhibition, underscoring the OFC's pivotal role in regulating depressive states. This research unveils THz photoneuromodulation as a promising, safe, rapid-acting, and durable neurotherapeutic strategy addressing persistent unmet needs in depression treatment.

Extensive segment floating will result in segment dislocation, crack, and leakage, posing significant risks of engineering accidents. It is important to control the segment floating based on adjusting shield operational parameters finely. A knowledge-based intelligence method designed for controlling segment floating is proposed in this study. Leveraging prior knowledge in segment floating, the framework of the intelligence method is constructed. This framework consists of a segment floating prediction model along with two auxiliary models. The segment floating prediction model considers the spatial and temporal characteristics of the shield operational parameters, including the early activation of the shield excavation parameters and the hysteretic nature of tail grouting parameters. The segment floating prediction model is the basis of the knowledge-based intelligence method. A multi-ring optimization strategy is designed to solve the conflict between the optimization results of adjacent rings. The case study shows that the segment floating prediction model has high prediction accuracy due to consideration of the spatial and temporal characteristics of the shield operational parameters. Considering the performance and computation cost, the optimal parameter configuration is figured out.

WASHINGTON, D.C. — The U.S. Naval Research Laboratory (NRL) has announced a new strategic direction for its NIKE laser-target facility to align its world-class capabilities with the Department of Defense’s (DoD) nuclear strategic priorities.

A new technique maintains structural integrity by essentially hiding openings in materials from surrounding forces. Rather than reinforcing an opening, such as a hole or a window, to protect against a few select forces, the new approach reorganizes nearly any set of forces that could affect the surrounding material to avoid the opening.

A new paper published in Chemistry of Materials puts artificial intelligence and machine learning on the task of finding new, powerful electrolytes for designing next-generation batteries for electric vehicles, phones, laptops and grid-scale energy storage. The paper from the University of Chicago Pritzker School of Molecular Engineering outlines a new framework for finding molecules that maximize three components that make an ideal battery electrolyte – ionic conductivity, oxidative stability and Coulombic efficiency.