Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the targetsensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated.

Background: Recent studies have shown that early diagnosis and intervention promote the patient's good prognosis. For patients who underwent cardiac surgery and require extracorporeal circulation support, the incidence of postoperative cognitive dysfunction (POCD) is higher than in other types of surgery due to greater changes in brain perfusion compared with normal physiological conditions. Recent studies have confirmed that the use of ulinastatin or dexmedetomidine in the perioperative period effectively reduces the incidence of POCD. In this study, ulinastatin was combined with dexmedetomidine to assess whether the combination of the two drugs could reduce the incidence of POCD. Methods: One hundred and eighty patients with heart valve replacement surgery undergoing cardiopulmonary bypass from August 2017 to December 2018 were enrolled, with age 60-80 years, American Society of Anesthesiologists (ASA) grades I-III, education level above elementary school, and either gender. According to the random number table method, patients were grouped into ulinastatin + dexmedetomidine (U+D) group, ulinastatin (U) group, dexmedetomidine (D) group, and normal saline (N) control group. Group U was pumped 20,000 UI/kg immediately after induction and the first day after surgery, group D continued to pump 0.4 µg/kg/h from induction to 2 h before extubation, group U+D dexmedetomidine 0.4 µg/kg/h + ulinastatin 20,000 UI/kg, and group N equal volume of physiological saline. The patients were enrolled with Mini-Mental State Examination (MMSE) before surgery. The cognitive function was assessed by Montreal Cognitive Assessment (MoCA) on the first day before surgery and on the seventh day after surgery. Inflammatory factors, such as S100β protein, interleukin (IL)-6, matrix metalloproteinase (MMP)-9, and tumor necrosis factor (TNF)-α, were detected in peripheral blood before anesthesia (T0), immediately after surgery (T1), and immediately after extubation (T2). Results: One hundred and fifty-four patients enrolled in this study. Compared with group N, the incidence of POCD in group U+D was the lowest (P < 0.05), followed by group U and group D. Group U+D had the lowest concentration of inflammatory factors at the T1 and T2 time points, followed by group U and group D. Zhou et al. Ulinastatin and Dexmedetomidine for Postoperative Cognitive Dysfunction Conclusions: Both ulinastatin and dexmedetomidine can reduce the perioperative inflammatory response and the incidence of POCD in patients with heart valve surgery, and their combination can better reduce the incidence of POCD.

The serum levels of pro-inflammatory marker IL-6 and S-100β protein increased after total hip-replacement in elderly patients, and such increase may serve as predicting parameters for the occurrence of POCD.

To reveal information hiding in link space of bibliographical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting the citation relationship between a query paper and a set of previous papers. Considering the gigantic size of relevant papers, the loosely connected citation network structure as well as the highly skewed citation relation distribution, citation prediction is more challenging than other link prediction problems which have been studied before. By building a meta-path based prediction model on a topic discriminative search space, we here propose a two-phase citation probability learning approach, in order to predict citation relationship effectively and efficiently. Experiments are performed on real-world dataset with comprehensive measurements, which demonstrate that our framework has substantial advantages over commonly used link prediction approaches in predicting citation relations in bibliographical networks.

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Abstract. This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency.

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The liver has recently been identified as a major organ for destruction of desialylated platelets. However, the underlying mechanism remains unclear. Kupffer cells, which are professional phagocytic cells in the liver, comprise the largest population of resident tissue macrophages in the body. Kupffer cells express a C-type lectin receptor, CLEC4F, that recognizes desialylated glycans with an unclear in vivo role in mediating platelet destruction. In this study, we generated a CLEC4F-deficient mouse model (Clec4f−/−) and found that CLEC4F was specifically expressed by Kupffer cells. Using the Clec4f−/− mice and a newly generated platelet-specific reporter mouse line, we revealed a critical role for CLEC4F on Kupffer cells in mediating destruction of desialylated platelets in the liver in vivo. Platelet clearance experiments and ultrastructural analysis revealed that desialylated platelets were phagocytized predominantly by Kupffer cells in a CLEC4F-dependent manner in mice. Collectively, these findings identify CLEC4F as a Kupffer cell receptor important for the destruction of desialylated platelets induced by bacteria-derived neuraminidases, which provide new insights into the pathogenesis of thrombocytopenia in disease conditions such as sepsis.