Self-adaptation-based dynamic coalition formation in a distributed agent network: a mechanism and a brief survey

In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies

Graph Self-Contrast Representation Learning

Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one positive and K negative samples for each graph, but it is difficult to set K. For those methods that do not use negative samples, it is often necessary to add additional strategies to avoid model collapse, which could only alleviate the problem to some extent. All these drawbacks will undoubtedly have an adverse impact on the generalizability and efficiency of the model. In this paper, to address these issues, we propose a novel graph self-contrast framework GraphSC, which only uses one positive and one negative sample, and chooses triplet loss as the objective. Specifically, self-contrast has two implications. First, GraphSC generates both positive and negative views of a graph sample from the graph itself via graph augmentation functions of various intensities, and use them for self-contrast. Second, GraphSC uses Hilbert-Schmidt Independence Criterion (HSIC) to factorize the representations into multiple factors and proposes a masked self-contrast mechanism to better separate positive and negative samples. Further, Since the triplet loss only optimizes the relative distance between the anchor and its positive/negative samples, it is difficult to ensure the absolute distance between the anchor and positive sample. Therefore, we explicitly reduced the absolute distance between the anchor and positive sample to accelerate convergence. Finally, we conduct extensive experiments to evaluate the performance of GraphSC against 19 other state-of-the-art methods in both unsupervised and transfer learning settings.Comment: ICDM 2023(Regular

Temperature controlled microcapsule loaded with Perilla essential oil and its application in preservation of peaches

In this study, Perilla frutescens essential oil (PEO) loaded microcapsules (PEOM) were successfully prepared and their thermal stability, temperature-responsive releasing effect, antioxidant activity, antibacterial activity, and preservation of peach were systematically investigated. PEOM showed excellent encapsulation efficiency (91.5%) with a core-shell ratio of 1.4:1 and exhibited high thermal stability, indicating that PEOM could effectively maintain PEO release rate. In vitro assays indicated that the optimal kinetic model for PEO release fitted well with first order with a diffusion mechanism. A high level of antioxidant and antibacterial activity of PEOM was maintained. In addition, owing to its sustained release, PEOM could prolong the shelf life of peaches significantly. Therefore, PEOM has potential application and development prospects in the field of food preservation

Case Report: Successful treatment of advanced hepatocarcinoma with the PD-1 inhibitor Camrelizumab

Primary liver cancer is characterized by closely related with chronic liver inflammation, thereby reversing hypoxic immunosuppressive microenvironment of tumor cell growth by immunotherapy drug is a potentially effective strategy. Camrelizumab is an anti-PD-1 antibody being developed by Jiangsu Hengrui Pharmaceuticals Co., Ltd. We reported a case of an adult critical Chinese patient with primary hepatocellular carcinoma and lung metastasis completely responding to Camrelizumab, most of the lesions were stable and no new lesions occurred after 1-year treatment, which provides us to reconsider the therapeutic effect of Camrelizumab on such patients. Camrelizumab had a safety profile for the patient in our case report, except for the occurrence of RCCEP. This case provides the evidence of the effective antitumor activity and manageable toxicities of Camrelizumab for patients with advanced hepatocellular carcinoma, which was the first application as far as we know

Loss of Angiopoietin-like 7 diminishes the regeneration capacity of hematopoietic stem and progenitor cells

© 2015 Xiao et al.; licensee Biomed Central. Successful expansion of hematopoietic stem cells (HSCs) would benefit the use of HSC transplants in the clinic. Angiopoietin-like 7 promotes the expansion of hematopoietic stem and progenitor cells (HSPC) in vitro and ex vivo. However, the impact of loss of Angptl7 on HSPCs in vivo has not been characterized. Here, we generated Angptl7-deficient mice by TALEN-mediated gene targeting and found that HSC compartments in Angptl7-null mice were compromised. In addition, wild type (WT) HSPCs failed to repopulate in the BM of Angptl7-null mice after serial transplantations while the engraftment of Angptl7-deficient HSPCs in WT mice was not impaired. These results suggest that Angptl7 is required for HSPCs repopulation in a non-cell autonomous manner.Link_to_subscribed_fulltex

Consensus interpretation of the p.Met34Thr and p.Val37Ile variants in GJB2 by the ClinGen Hearing Loss Expert Panel

Purpose: Pathogenic variants in GJB2 are the most common cause of autosomal recessive sensorineural hearing loss. The classification of c.101T>C/p.Met34Thr and c.109G>A/p.Val37Ile in GJB2 are controversial. Therefore, an expert consensus is required for the interpretation of these two variants. Methods: The ClinGen Hearing Loss Expert Panel collected published data and shared unpublished information from contributing laboratories and clinics regarding the two variants. Functional, computational, allelic, and segregation data were also obtained. Case-control statistical analyses were performed. Results: The panel reviewed the synthesized information, and classified the p.Met34Thr and p.Val37Ile variants utilizing professional variant interpretation guidelines and professional judgment. We found that p.Met34Thr and p.Val37Ile are significantly overrepresented in hearing loss patients, compared with population controls. Individuals homozygous or compound heterozygous for p.Met34Thr or p.Val37Ile typically manifest mild to moderate hearing loss. Several other types of evidence also support pathogenic roles for these two variants. Conclusion: Resolving controversies in variant classification requires coordinated effort among a panel of international multi-institutional experts to share data, standardize classification guidelines, review evidence, and reach a consensus. We concluded that p.Met34Thr and p.Val37Ile variants in GJB2 are pathogenic for autosomal recessive nonsyndromic hearing loss with variable expressivity and incomplete penetrance