Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation

Cognitive abilities, such as Theory of Mind (ToM), play a vital role in facilitating cooperation in human social interactions. However, our study reveals that agents with higher ToM abilities may not necessarily exhibit better cooperative behavior compared to those with lower ToM abilities. To address this challenge, we propose a novel matching coalition mechanism that leverages the strengths of agents with different ToM levels by explicitly considering belief alignment and specialized abilities when forming coalitions. Our proposed matching algorithm seeks to find stable coalitions that maximize the potential for cooperative behavior and ensure long-term viability. By incorporating cognitive insights into the design of multi-agent systems, our work demonstrates the potential of leveraging ToM to create more sophisticated and human-like coordination strategies that foster cooperation and improve overall system performance

Implications of Canonical Gauge Coupling Unification in High-Scale Supersymmetry Breaking

We systematically construct two kinds of models with canonical gauge coupling unification and universal high-scale supersymmetry breaking. In the first we introduce standard vector-like particles while in the second we also include non-standard vector-like particles. We require that the gauge coupling unification scale is from 5 x 10^{15} GeV to the Planck scale, that the universal supersymmetry breaking scale is from 10 TeV to the unification scale, and that the masses of the vector-like particles (M_V) are universal and in the range from 200 GeV to 1 TeV. Using two-loop renormalization group equation (RGE) running for the gauge couplings and one-loop RGE running for Yukawa couplings and the Higgs quartic coupling, we calculate the supersymmetry breaking scales, the gauge coupling unification scales, and the corresponding Higgs mass ranges. When the vector-like particle masses are less than 1 TeV, these models can be tested at the LHC.Comment: 25 pages, 4 figure

Effect of Kang Fu Yan capsule on phenol mucilage-induced intrauterine adhesion injury in female rats

Purpose: To investigate the effect of Kang fu yan capsule (KFYC) on phenol mucilage-induced intrauterine adhesion (IUA) in a rat model, and the underlying mechanisms. Methods: An IUA model was established by injecting 0.06 mL of 25 % phenol mucilage into the uterus of female Sprague-Dawley rats. The IUA model rats (n=59) were randomly divided into 5 groups: IUA group, fuke qianjin tablet group (FKQJT, 0.22 mg/kg), and 3 KFYC groups given different doses of the drug i.e. 0.13, 0.39and 1.17 mg/kg. A group of 10 healthy female rats served as control. After 19 days treatment, blood samples were collected for determination of IL-2 and IL-10 by ELISA, while uterine tissues were subjected to histological examination using hematoxylin and eosin staining (H&E) and Masson staining. Expressions of Notch1, recombination signal binding protein-JK (RBP-JK), a disintegrin and metalloprotease (ADAM)-12, ADAM-15, matrix metalloprotein-9 (MMP-9), and inhibitor of NF-κB (IĸB) in uterine tissues were determined using RT-qPCR and western blot analysis. Results: Compared to IUA group, histological results showed reduced inflammatory cell infiltration in rat uterine tissue of KFYC group. Moreover, KFYC significantly reversed uterine fibrosis (p < 0.05). Serum concentrations of IL-2 significantly decreased in KFYC groups (p < 0.05 or p < 0.01), and there was significant increases the serum concentrations of IL-10 in KFYC groups (p < 0.05 or < 0.01), when compared to IUA group. The mRNA and protein expressions of Notch1, RBP-JK, ADAM-12, ADAM-15, MMP-9 were also significantly down-regulated (p < 0.05), while protein expression of IĸB was upregulated in KFYC group, when compared to IUA group. Conclusion: KFYC exerts an anti-IUA effect via amelioration of uterine inflammation and fibrosis, probably via a mechanism involving regulation of Notch1/ADAM pathway

MRGPRX4 is a bile acid receptor for human cholestatic itch.

Patients with liver diseases often suffer from chronic itch, yet the pruritogen(s) and receptor(s) remain largely elusive. Here, we identify bile acids as natural ligands for MRGPRX4. MRGPRX4 is expressed in human dorsal root ganglion (hDRG) neurons and co-expresses with itch receptor HRH1. Bile acids elicited Ca2+ responses in cultured hDRG neurons, and bile acids or a MRGPRX4 specific agonist induced itch in human subjects. However, a specific agonist for another bile acid receptor TGR5 failed to induce itch in human subjects and we find that human TGR5 is not expressed in hDRG neurons. Finally, we show positive correlation between cholestatic itch and plasma bile acids level in itchy patients and the elevated bile acids is sufficient to activate MRGPRX4. Taken together, our data strongly suggest that MRGPRX4 is a novel bile acid receptor that likely underlies cholestatic itch in human, providing a promising new drug target for anti-itch therapies

Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia

Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient’s PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment

Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications