Inhibitory effect of Jeju endemic seaweeds on the production of pro-inflammatory mediators in mouse macrophage cell line RAW 264.7

Seaweed has been used in traditional cosmetics and as a herbal medicine in treatments for cough,boils,goiters,stomach ailments,and urinary diseases,and for reducing the incidence of tumors,ulcers,and headaches.Despite the fact that seaweeds are frequently used in the practice of human health,little is known about the role of seaweed in the context of inflammation.This study aimed to investigate the influence of Jeju endemic seaweed on a mouse macrophage cell line(RAW 264.7) under the stimulation of lipopolysaccharide(LPS).Ethyl acetate extracts obtained from 14 different kinds of Jeju seaweeds were screened for inhibitory effects on pro-inflammatory mediators.Our results revealed that extracts from five seaweeds,Laurencia okamurae,Grateloupia elliptica,Sargassum thun-bergii,Gloiopeltis furcata,and Hizikia fusiformis,were potent inhibitors of the production of pro-inflammatory mediators such as nitric oxide(NO),prostaglandin E2(PGE2),interleukin-6(IL-6),and tumor necrosis factor-α(TNF-α).Based on these results,the anti-inflammatory effects and low cell toxicity of these seaweed extracts suggest potential thera-peutic applications in the regulation of the inflammatory response.     

Mutual-reinforcement document summarization using embedded graph based sentence clustering for storytelling

In this paper, a document summarization framework for storytelling is proposed to extract essential sentences from a document by exploiting the mutual effects between terms, sentences and clusters. There are three phrases in the framework: document modeling, sentence clustering and sentence ranking. The story document is modeled by a weighted graph with vertexes that represent sentences of the document. The sentences are clustered into different groups to find the latent topics in the story. To alleviate the influence of unrelated sentences in clustering, an embedding process is employed to optimize the document model. The sentences are then ranked according to the mutual effect between terms, sentence as well as clusters, and high-ranked sentences are selected to comprise the summarization of the document. The experimental results on the Document Understanding Conference (DUC) data sets demonstrate the effectiveness of the proposed method in document summarization. The results also show that the embedding process for sentence clustering render the system more robust with respect to different cluster numbers.     

Improving educational assessment： A computer-adaptive multiple choice assessment using NRET as the scoring method

Assessment is central to any educational process. Number Right （NR） scoring method is a conventional scoring method for multiple choice items, where students need to pick one option as the correct answer. One point is awarded for the correct response and zero for any other responses. However, it has been heavily criticized for guessing and failure to credit partial knowledge. Despite continued research in scoring methods, conventional NR scoring method remains the most popular and widely used procedure. Thus, the problem of crediting partial knowledge and guessing still exist. In addition, the current assessment practices do not take students＇ misconceptions into consideration. Thus, there is a need to propose a new scoring method that is able to minimize guessing, credit partial knowledge, yet able to diagnose misconceptions. This study proposed the development of a Computer-Adaptive Assessment Software （CAAS） for MC items using the new Number Right Elimination Testing （NRET） which take into consideration these three elements. This paper described the rational for using the NRET, the design and development of CAAS and the results of the pilot study carried out in three secondary schools in Malaysia.     

Using the British Education Index to Survey the Field of Educational Studies

Bibliographic records published by the British Education Index (BEI) between 1957 and 2000 are analysed in the context of a history of the BEI's changing presentation of information about the field. The value of frequency counts for BEI subject terms is discussed, in relation to their potential for revealing trends in the fields of educational studies and information management.     

TV's distorted vision of poverty

Avoiding almost entirely the depiction of poverty during prime‐time broadcasts, television networks present a sentimentalized vision of economic deprivation that omits or minimizes hardship while idealizing the supposed benefits of a spartan way of life. Much happier than the harried members of middle‐ and upper‐income groups, poor and working people on television seldom strive against their economic fates or against the system. Conclusions are based on a monitoring of prime‐time entertainment broadcast by the three commercial networks during “ratings month.”