Huaibei Mining (China)

Bacterial contamination is a thorny issue for public health protection in the 21st century, which causes a huge threat to human life and economic development. Photocatalytic disinfection has been verified as a promising technique to address the issue owing to its inexpensive, green, and sustainable process. Nevertheless, continuous external light illumination is indispensable for the photocatalytic disinfection reaction, which becomes an Achilles’ Heel for the practical application of this technique. Long afterglow luminescent material with a unique “light storage” effect can effectively support photocatalytic disinfection reaction under conditions of no light or even darkness, brightening the prospect of continuous bacteria inactivation. Herein, we critically envisage the perspective of bacterial disinfection by long afterglow luminescent material, and outlook the ongoing challenge and study direction of the material fabrication, mechanistic exploration, and disinfection applications in this frontier field.

A deformed Donaldson–Thomas (dDT) connection is a Hermitian connection of a Hermitian line bundle over a G 2 -manifold X satisfying a certain nonlinear PDE. This is considered to be the mirror of a (co)associative cycle in the context of mirror symmetry. It can also be considered as an analogue of a G 2 -instanton. In this paper, we see that some important observations that appear in other geometric problems are also found in the dDT case as follows. (1) A dDT connection exists if a 7-manifold has full holonomy G 2 and the G 2 -structure is “sufficiently large”. (2) The dDT equation is described as the zero of a certain multi-moment map. (3) The gradient flow equation of a Chern–Simons type functional of Karigiannis and Leung, whose critical points are dDT connections, agrees with the Spin ( 7 ) version of the dDT equation on a cylinder with respect to a certain metric on a certain space. This can be considered as an analogue of the observation in instanton Floer homology for 3-manifolds.

It has been proven that periconceptional folic acid (FA) supplementation could prevent neural tube defects. However, FA supplementation during different stages of pregnancy and its association with the neurobehavioral development of offspring remains unclear, particularly the effects of continued FA supplementation during the second and third trimesters. This study aimed to investigate the relationship between maternal FA supplementation at various stages of pregnancy and infant neurobehavioral development. In a prospective birth cohort study involving 3246 parent-child pairs, 2905 infants completed neurobehavioral development assessments at 6 months and 3005 infants at 18 months. Information regarding micronutrient supplementation at various stages of pregnancy was recorded. Multivariable logistic regression was used to evaluate the association between FA supplementation and infant neurobehavioral developmental delays. In addition, propensity score analysis was performed to correct the potential imbalances in the distribution of related factors between the groups. During the periconceptional period, maternal standardized FA supplementation (taking 0.4 mg FA daily before and in early pregnancy, as officially recommended) was associated with a reduced risk of possible development delays in 18-month-old infants in the communication domain (RR = 0.48, 95% CI: 0.26-0.86; P = 0.015). After the 12th gestational week, maternal continuous FA supplementation in the second and third trimesters was significantly associated with a decreased risk of possible neurobehavioral development delay in 6-month-old infants in fine motor domain (RR = 0.31, 95% CI: 0.12-0.82; P = 0.019) and the problem-solving domain (RR = 0.20, 95% CI: 0.05-0.79; P = 0.022). These associations remained significant after adjusting for confounders and propensity scores. FA supplementation at different stages of pregnancy may enhance neurobehavioral development in offspring. To confirm these findings, additional investigations or trials with larger sample sizes and consistent tracking of folate status throughout pregnancy are recommended.

This paper proposes Prototypical Guidance SAM (ProtoSAM), a novel semi-supervised framework for 3D medical image segmentation. To address the limitations of existing methods in generalization under limited annotations and high model complexity, our framework introduces a Token Prototype Encoder that replaces parametric clustering with dynamic prototype optimization through fuzzy assignment and momentum update mechanisms. The Prototype-adaptive Decoder explicitly integrates multi-scale clustering features and employs morphological post-processing to refine boundary accuracy. Experiments show that ProtoSAM achieves Dice scores of 0.78 and 0.55, and HD95 scores of 3.33 and 17.97 on two large public datasets. By generating high-quality pseudo-labels through knowledge prototypes, our method maintains performance comparable to fully-supervised using merely 33% labeled data, significantly reducing dependency on complete annotations. Code is available at: https://github.com/yanliugroup.

Evaluating node importance in integrated complex networks holds significant practical value. Existing methods and metrics predominantly focus on single-layer complex networks, exhibiting limited applicability to integrated directed weighted networks and often failing to comprehensively and objectively reflect the true structure of such integrated networks. To address this, this paper proposes a node importance evaluation method for integrated directed weighted networks based on PageRank. This method defines node attribute metrics for integrated directed weighted networks and integrates them with the PageRank algorithm to construct a novel node importance evaluation metric, IDWCN. This metric simultaneously characterizes the local connectivity, global linkage relationships, and node hierarchy attributes within the integrated network, thereby enhancing the accuracy of node importance evaluation. Simulation analysis on an improved integrated directed weighted kite network model demonstrates that the proposed method effectively captures the importance of nodes in integrated directed weighted networks. Furthermore, experiments conducted on a real-world integrated social network dataset confirm that this method accurately identifies critical nodes within the network, validating its applicability and effectiveness in complex network analysis.