The Systemic Evaluation and Clinical Significance of Immunological Function for Advanced Lung Cancer Patients

Background and objective The actual evaluation of immunological function is significant for studing the tumor development and devising a treatment in time. The aim of this study is to evaluate the immunological function of advanced lung cancer patients systematically, and to discuss the clinical significance. Methods The nucleated cell amounts of advanced lung cancer patients and the healthy individuals were counted. The immune cell subsets and the levels of IL-4, INF-γ, perforin and granzyme in CD8+T cells by the flow cytometry were measured. The proliferation activity and the inhibition ratio of immune cells to several tumor cell lines were evaluated by MTT assay. Results The absolute amounts and subsets of T, B, NK cells of advanced lung cancer patients were lower than the healthy individuals (P < 0.05); However, the proportion of regulatory T cells of advanced lung cancer patients (4.00±1.84)% was lower than the healthy individuals (1.27±0.78)% (P < 0.05). The positive rates of IFN-γ perforin, granzyme in CD8+T cells decreased while them in IL-4 did not in the advanced lung cancer patients compared to the healthy control group (P < 0.05). The proliferation activity of immune cells, the positive rate of PPD masculine and the inhibition ratio to tumor cells in the advanced lung cancer patients was lower than the healthy subsets obviously (P < 0.05). Conclusion There was a significant immune depression in the advanced lung cancer patients compared to the healthy individuals

Learning Local Components to Understand Large Bayesian Networks

Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most interrelevant attributes in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data

Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Structured Memetic Automation for Online Human-like Social Behavior Learning

Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior

A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems

Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains

On Information Coverage for Location Category Based Point-of-Interest Recommendation

Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms

Optimal Route Search with the Coverage of Users' Preferences

The preferences of users are important in route search and planning. Users may also weight their preferences differently. For example, when a user plans a trip within a city, their preferences can be expressed as keywords shopping mall, restaurant, and museum, with weights 0.5, 0.4, and 0.1, respectively. The resulting route should best satisfy their weighted preferences. In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint. We prove that this problem is NP-hard. To solve the complex problem, we propose the optimal route search by adapting the A* algorithm. An admissible heuristic function is developed to preserve the solution optimality. The experiments conducted on real-world datasets demonstrate both the efficiency and accuracy of our proposed algorithms

A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems

Memetic multiagent system emerges as an enhanced version of multiagent systems with the implementation of meme inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins’ notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing human-like agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains