Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between health- care service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the e±ciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents

Silencing of acetyl-CoA carboxylase-α gene in human gastric cancer cells inhibits proliferation via induction of apoptosis, autophagy and suppression of cell invasion

Purpose: To study the role and therapeutic potential of acetyl-CoA-carboxylase-α (ACC) in the management of gastric cancer. Methods: Expression of ACC in gastric cancer cell lines was determined using quantitative real-time polymerase chain reaction (qRT-PCR). Lipofectamine 2000 reagent was used for transfection, while cell viability was determined by MTT assay. Apoptotic cell death was assayed with 4′, 6-diamidino-2- phenylindole (DAPI) and acridine orange/ethidium bromide (AO/EB) staining. The proportion of apoptotic cells was estimated with Annexin V/PI staining. Wound healing and Transwell assays were employed to monitor cell migration and invasion, while protein expression was determined using western blotting. Results: The results showed that ACC was significantly enhanced in SNU-1 gastric cancer cells (4.2- fold). Silencing of ACC in SNU-1 gastric cancer cells caused significant decrease in cell proliferation (p < 0.05). Electron microscopy examination showed that ACC silencing triggered autophagic cell death in SNU-1 cells, and increased expression of LC3 II. Results from DAPI and AO/EB assays demonstrated that ACC silencing also promoted apoptosis in SNU-1 gastric cancer cells. Annexin V/PI assay results revealed that apoptotic cell population increased from 2.7 to 13.8 % due to ACC silencing (p < 0.05). Moreover, Bax expression increased, while Bcl-2 expression decreased upon ACC silencing. Transwell assay results indicate that ACC silencing caused marked decrease in the invasion of the SNU-1 cells and downregulation of the expressions of MMP-2 and MMP-9 (p < 0.05). Conclusion: ACC is likely to be an important therapeutic target for gastric cancer

Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data

peer reviewedThe application of empirical methods for satellite-derived bathymetry is limited by the lack of in situ bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the ${{\bm{R}}}^2$ value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps

Wip1-dependent modulation of macrophage migration and phagocytosis

Macrophage accumulation within the vascular wall is a hallmark of atherosclerosis. Controlling macrophage conversion into foam cells remains a major challenge for treatment of atherosclerotic diseases. Here, we show that Wip1, a member of the PP2C family of Ser/Thr protein phosphatases, modulates macrophage migration and phagocytosis associated with atherosclerotic plaque formation. Wip1 deficiency increases migratory and phagocytic activities of the macrophage under stress conditions. Enhanced migration of Wip1-/- macrophages is mediated by Rac1-GTPase and PI3K/AKT signalling pathways. Elevated phagocytic ability of Wip1-/- macrophages is linked to CD36 plasma membrane recruitment that is regulated by AMPK activity. Our study identifies Wip1 as an intrinsic negative regulator of macrophage chemotaxis. We propose that Wip1-dependent control of macrophage function may provide avenues for preventing or eliminating plaque formation in atherosclerosis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Argumentative Learning with Intelligent Agents

Argumentation plays an important role in information sharing, deep learning and knowledge construction. However, because of the high dependency on qualified arguing peers, argumentative learning has only had limited applications in school contexts to date. Intelligent agents have been proposed as virtual peers in recent research and they exhibit many benefits for learning. Argumentation support systems have also been developed to support learning through human-human argumentation. Unfortunately these systems cannot conduct automated argumentations with human learners due to the difficulties in modeling human cognition. A gap exists between the needs of virtual arguing peers and the lack of computing systems that are able to conduct human−computer argumentation. This research aimed to fill the gap by designing computing models for automated argumentation, develop a learning system with virtual peers that can argue automatically and study argumentative learning with virtual peers

Fuzzy cognitive modeling for argumentative agent

Argumentation plays an important role in promoting deep learning, fostering conceptual change and supporting problem solving. The new “learning by arguing” paradigm leads to new learning opportunities. However, due to the difficulties in modeling human cognition, there are few learning systems that can facilitate argumentation dialogues between systems and learners. Fuzzy Cognitive Map (FCM) is an effective tool in modeling human cognition. This paper proposes an FCM based argumentation model. Based on this model we design an argumentative software agent to facilitate argumentative learning. Provided with the domain knowledge and argumentation capability, the agent is able to simulate a peer learner and automatically conduct argumentative dialogues with learners. The argumentative agent can be applied in general school education as well as special domains like diabetes education and eHealth decision support

Web User Modeling via Negotiating Information Foraging Agent

Information foraging theory lays a good foundation for web user modeling. However, the existing user modeling methods mainly focus on fixed information needs. In the real world, a user’s information goal often evolves, and information foraging is a negotiation process between the user and the system. In this paper, we proposed an agent based approach that modeled the dynamic information seeking process of information foragers