Nuclear factor κB (NF-κB) is a family of inducible transcription factors that plays a vital role in different aspects of immune responses. NF-κB is normally sequestered in the cytoplasm as inactive complexes via physical association with inhibitory proteins termed IκBs. In response to immune and stress stimuli, NF-κB members become activated via two major signaling pathways, the canonical and noncanonical pathways, and move to the nucleus to exert transcriptional functions. NF-κB is vital for normal immune responses against infections, but deregulated NF-κB activation is a major cause of inflammatory diseases. Accumulated studies suggest the involvement of NF-κB in the pathogenesis of renal inflammation caused by infection, injury, or autoimmune factors. In this review, we discuss the current understanding regarding the activation and function of NF-κB in different types of kidney diseases.

In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction. In doing so, a direction vector is introduced for each word, whose embedding is thus learned by not only word co-occurrence patterns in its context, but also the directions of its contextual words. Theoretical and empirical studies on complexity illustrate that our model can be trained as efficient as the original skip-gram model, when compared to other extensions of the skip-gram model. Experimental results show that our model outperforms others on different datasets in semantic (word similarity measurement) and syntactic (partof-speech tagging) evaluations, respectively.

Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.

Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing (NLP) applications, but also facilitates reading and understanding of running texts in peoples' daily lives. However, to utilize datadriven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCRand ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach. * This work was conducted during Dingmin Wang's internship in Tencent AI Lab. Sentence Correction 我们应该认真对待这些 己 (ji2) 经发生的事 已 (yi3) 在我们班上， 她 (ta1)是一个很聪明的男孩 他 (ta1)

Bone metastasis, a clinical complication of patients with advanced breast cancer, seriously reduces the quality of life. To avoid destruction of the bone matrix, current treatments focus on inhibiting the cancer cell growth and the osteoclast activity through combination therapy. Therefore, it could be beneficial to develop a bone-targeted drug delivery system to treat bone metastasis. Here, a bone-targeted nanoplatform was developed using gold nanorods enclosed inside mesoporous silica nanoparticles (Au@MSNs) which were then conjugated with zoledronic acid (ZOL). The nanoparticles (Au@MSNs-ZOL) not only showed bone-targeting ability in vivo but also inhibited the formation of osteoclast-like cells and promoted osteoblast differentiation in vitro. The combination of Au@MSNs-ZOL and photothermal therapy (PTT), triggered by near-infrared irradiation, inhibited tumor growth both in vitro and in vivo and relieved pain and bone resorption in vivo by inducing apoptosis in cancer cells and improving the bone microenvironment. This single nanoplatform combines ZOL and PTT to provide an exciting strategy for treating breast cancer bone metastasis.

It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.

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