No abstract

With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiplelanguage context.

Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance.

Nonalcoholic steatohepatitis (NASH) is a progressive disease that is often accompanied by metabolic syndrome and poses a high risk of severe liver damage. However, no effective pharmacological treatment is currently available for NASH. Here we report that CASP8 and FADD-like apoptosis regulator (CFLAR) is a key suppressor of steatohepatitis and its metabolic disorders. We provide mechanistic evidence that CFLAR directly targets the kinase MAP3K5 (also known as ASK1) and interrupts its N-terminus-mediated dimerization, thereby blocking signaling involving ASK1 and the kinase MAPK8 (also known as JNK1). Furthermore, we identified a small peptide segment in CFLAR that effectively attenuates the progression of steatohepatitis and metabolic disorders in both mice and monkeys by disrupting the N-terminus-mediated dimerization of ASK1 when the peptide is expressed from an injected adenovirus-associated virus 8-based vector. Taken together, these findings establish CFLAR as a key suppressor of steatohepatitis and indicate that the development of CFLAR-peptide-mimicking drugs and the screening of small-molecular inhibitors that specifically block ASK1 dimerization are new and feasible approaches for NASH treatment.

Hepatic ischemia-reperfusion (IR) injury is a common clinical issue lacking effective therapy and validated pharmacological targets. Here, using integrative 'omics' analysis, we identified an arachidonate 12-lipoxygenase (ALOX12)-12-hydroxyeicosatetraenoic acid (12-HETE)-G-protein-coupled receptor 31 (GPR31) signaling axis as a key determinant of the hepatic IR process. We found that ALOX12 was markedly upregulated in hepatocytes during ischemia to promote 12-HETE accumulation and that 12-HETE then directly binds to GPR31, triggering an inflammatory response that exacerbates liver damage. Notably, blocking 12-HETE production inhibits IR-induced liver dysfunction, inflammation and cell death in mice and pigs. Furthermore, we established a nonhuman primate hepatic IR model that closely recapitulates clinical liver dysfunction following liver resection. Most strikingly, blocking 12-HETE accumulation effectively attenuated all pathologies of hepatic IR in this model. Collectively, this study has revealed previously uncharacterized metabolic reprogramming involving an ALOX12-12-HETE-GPR31 axis that functionally determines hepatic IR procession. We have also provided proof of concept that blocking 12-HETE production is a promising strategy for preventing and treating IR-induced liver damage.