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Abstract This study aimed to investigate the relationship between hemodialysis duration (HDD) and retinal nerve fiber layer (RNFL) thickness. A total of 60 patients receiving maintenance hemodialysis and 67 healthy controls were analyzed. Spectral domain optical coherence tomography (SD-OCT) was employed to measure annular RNFL thicknesses. The hemodialysis group exhibited reduced temporal and inferior RNFL thicknesses relative to the control group. In hemodialysis patients, the inferior RNFL thickness was negatively correlated with HDD and positively correlated with intraocular pressure (IOP). Moreover, IOP was positively correlated with HDD. Mediation analysis showed that the negative correlation between HDD and inferior RNFL thickness was mediated by IOP. In conclusion, hemodialysis leads to temporal and inferior RNFL thinning, and the thickness reduction is proportional to hemodialysis duration. However, such changes are not induced by an increase in IOP.

Highly robust networks can resist attacks, as when some UAVs fail, the remaining UAVs can still transmit data to each other. In order to improve the robustness of a multi-UAV network, most methods construct the network by adjusting the positions of the UAVs and adding a large number of links. However, having a large number of links greatly consumes communication resources and increases serious signal interference. Therefore, it is necessary to study a method that can improve robustness and reduce the number of links. In this paper, we propose a method that consists of combining formation control and link selection, which can work in a distributed manner. For formation control, our method keeps the UAVs compact in the obstacle environment through an improved artificial potential field. The compact formation enables UAVs to have a large number of neighbors. For link selection, reinforcement learning is used to improve the robustness of the network and reduce the number of network edges. In the simulation of the 3D urban environment, three failure modes are used to verify the robustness of the network. The experimental results show that even if the number of links is reduced by 20%, the networks designed by our method still have strong robustness.

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.

Presently, in the context of the novel coronavirus pneumonia epidemic, several antibiotics are overused in hospitals, causing heavy pressure on the hospital’s wastewater treatment process. Therefore, developing stable, safe, and efficient hospital wastewater treatment equipment is crucial. Herein, a bench-scale electrooxidation equipment for hospital wastewater was used to evaluate the removal effect of the main antibiotic levofloxacin (LVX) in hospital wastewater using response surface methodology (RSM). During the degradation process, the influence of the following five factors on total organic carbon (TOC) removal was discussed and the best reaction condition was obtained: current density, initial pH, flow rate, chloride ion concentration, and reaction time of 39.6 A/m2, 6.5, 50 mL/min, 4‰, and 120 min, respectively. The TOC removal could reach 41% after a reaction time of 120 min, which was consistent with the result predicted by the response surface (40.48%). Moreover, the morphology and properties of the electrode were analyzed. The degradation pathway of LVX was analyzed using high-performance liquid chromatography–mass spectrometry (LC–MS). Subsequently, the bench-scale electrooxidation equipment was changed into onboard-scale electrooxidation equipment, and the onboard-scale equipment was promoted to several hospitals in Dalian.

To capture the stress corrosion cracking (SCC) in metallic materials, a phase‐field framework considering localized plastic deformation and stress states is established. A new function of critical energy release rate is proposed to describe the degradation of cracking resistance of materials in the SCC process. This proposed framework can reproduce SCC results consistent with experimental observations in C‐ring steel SCC tests, especially capturing the directionally characteristic of the cracks. Furthermore, the influences of localized plastic deformation and stress states on the SCC process are studied. This novel phase‐field framework can capture (1) the influences of coupled electricity, mechanics to the SCC mechanisms; (2) the contribution of plastic strain energy as the driving force for the phase‐field; (3) the potential and preferential crack initiation and propagation morphology within the complex stress and strain domain; and (4) the dependence of SCC rate and propagation direction on stress state and localized plastic deformation. [ABSTRACT FROM AUTHOR]

53BP1 is a well-established DNA damage repair factor that has recently emerged to critically regulate gene expression for tumor suppression and neural development. However, its precise function and regulatory mechanisms remain unclear. Here, we showed that phosphorylation of 53BP1 at serine 25 by ATM is required for neural progenitor cell proliferation and neuronal differentiation in cortical brain organoids. Dynamic phosphorylation of 53BP1-serine 25 controls 53BP1 target genes governing neuronal differentiation and function, cellular response to stress, and apoptosis. Mechanistically, ATM and RNF168 govern 53BP1's binding to gene loci to directly affect gene regulation, especially at genes for neuronal differentiation and maturation. 53BP1 serine 25 phosphorylation effectively impedes its binding to bivalent or H3K27me3-occupied promoters, especially at genes regulating H3K4 methylation, neuronal functions, and cell proliferation. Beyond 53BP1, ATM-dependent phosphorylation displays wide-ranging effects, regulating factors in neuronal differentiation, cytoskeleton, p53 regulation, as well as key signaling pathways such as ATM, BDNF, and WNT during cortical organoid differentiation. Together, our data suggest that the interplay between 53BP1 and ATM orchestrates essential genetic programs for cell morphogenesis, tissue organization, and developmental pathways crucial for human cortical development. 53BP1 is a DNA damage repair factor that regulates gene expression for tumor suppression and neural development, but its precise regulatory mechanisms are unclear. This study shows that phosphorylation of 53BP1 by ATM kinase is crucial for modulating genetic programs in neural progenitors and cortical organoids. [ABSTRACT FROM AUTHOR]

This study investigates the effects of forest aging on ectomycorrhizal (EcM) fungal community and foraging behavior and their interactions with plant–soil attributes. We explored EcM fungal communities and hyphal exploration types via rDNA sequencing and investigated their associations with plant–soil traits by comparing younger (~120 years) and older (~250 years) temperate forest stands in Northeast China. The results revealed increases in the EcM fungal richness and abundance with forest aging, paralleled by plant–soil feedback shifting from explorative to conservative nutrient use strategies. In the younger stands, Tomentella species were prevalent and showed positive correlations with nutrient availability in both the soil and leaves, alongside rapid increases in woody productivity. However, the older stands were marked by the dominance of the genera Inocybe, Hymenogaster, and Otidea which were significantly and positively correlated with soil nutrient contents and plant structural attributes such as the community-weighted mean height and standing biomass. Notably, the ratios of longer-to-shorter distance EcM fungal exploration types tended to decrease along with forest aging. Our findings underscore the integral role of EcM fungi in the aging processes of temperate forests, highlighting the EcM symbiont-mediated mechanisms adapting to nutrient scarcity and promoting sustainability in plant–soil consortia. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) continues to increase, more and more microgrids (MG) are interconnected with distribution system operators (DSO). To reduce system operational costs between MGs and DSOs, it is necessary to develop certain optimization strategies. This article proposes an optimized collaborative framework for modeling multi-entity distribution networks. In this model, DSOs are placed at the upper level to formulate policies, while MGs are at the lower level to respond in real-time to these policies. Furthermore, the multi-agent relationships in the model are described using Stackelberg game mechanisms, enhancing economic efficiency through dynamic gaming. Additionally, a data-driven multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is investigated to simulate the gaming process and improve the overall model's non-linear optimization capabilities. Considering that the simulation process can lead to violations of the energy storage system capacity constraints, a physics-based model is designed within the framework to ensure the safety of energy storage systems (ESS). Finally, compared to the MADDPG and penalty function methods, the proposed approach reduces the operational costs by 19.05%. [ABSTRACT FROM AUTHOR]