We have reported the usefulness of the subxiphoid video-assisted thoracoscopic surgery approach in thymectomy. However, such a new method may have unknown complications that rarely occur. The brachiocephalic vein has been considered to be the vessel that is most frequently injured when performing thymectomy because of the skeletal anatomy. We herein report a case of intraoperative injury of brachiocephalic vein using tubeless subxiphoid thoracoscopic thymectomy. No additional complications have been found in the 3 months since the operation. Though subxiphoid video-assisted thoracoscopic surgery thymectomy is a safe and less invasive operation, intraoperative complications are possible, and surgeons should express caution. (C) 2021 by The Society of Thoracic Surgeons

Objectives: This study aims to determine the association of EGFR/KRAS mutation status with histological subtypes of lung adenocarcinoma (LAC) based on the IASLC/ATS/ERS classification. Methods: Pubmed and Cochrane databases were searched from January 2011 to June 2018 for studies that included patients with LAC who underwent surgical resection were classified according to the new IASLC/ATS/ERS classification. EGFR/KRAS status assessment was requireded. The primary outcome was determined by the odds ratio (OR) of the incidence of mutation status of certain of each histological subtype. The reference group consisted of EGFR/KRAS mutation negative patients. Results: Twenty-seven eligible studies involving 9022 patients with mutation gene detection were included for analysis. Among them, 6717 (74.5%) patients were from the Asian region and, 2305 (25.5%) patients were from Non-Asian regions. The most prevalent subtype was acinar (34.7%), followed by papillary (22.9%), lepidic (18.9%), solid (13.6%), micropapillary (6.3%), and invasive mucinous adenocarcinoma (3.5%). EGFR mutations were more common in patients with resected lepidic predominant adenocarcinoma (OR,1.76; 95%CI, 1.38-2.24;p < 0.01) and were rarely found in solid predominant adenocarcinoma (OR,0.28; 95%CI, 0.23-0.34:p < 0.01) or IMA (OR,0.10; 95%CI, 0.06-0.14;p < 0.01). Conversely, KRAS mutations were characterized by IMA (OR,7.01; 95%CI, 5.11-9.62;p < 0.01), and were less frequently identified in lepidic (OR,0.58; 95%CI, 0.45-0.75:p < 0.01) and acinar (OR,0.65; 95%CI, 0.55-0.78;p < 0.01) predominant subtypes. Further analyses were performed in Asian and Non-Asian groups and the results were consistent. Conclusions: The current study confirms that the IASLC/ATS/ERS classification is associated with driver gene alterations in resected LAC. (C) 2019 Elsevier Ltd, BASO similar to The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

BACKGROUND:Mediastinal restaging after induction treatment is still a difficult and controversial issue. We aimed to investigate the diagnostic accuracy of endobronchial ultrasound-guided transbronchial needle aspiration and endoscopic ultrasound-guided fine-needle aspiration for restaging the mediastinum after induction treatment in patients with lung cancer.METHODS:Embase and PubMed databases were searched from conception to March 2019. Data from relevant studies were analyzed to assess sensitivity and specificity of endobronchial ultrasound-guided transbronchial needle aspiration and endoscopic ultrasound-guided fine-needle aspiration, and to fit the hierarchical summary receiver operating characteristic curves.RESULTS:A total of 10 studies consisting of 558 patients fulfilled the inclusion criteria. All patients were restaged by endobronchial ultrasound-guided transbronchial needle aspiration, endoscopic ultrasound-guided fine-needle aspiration, or both. Negative results were confirmed by subsequent surgical approaches. There were no complications reported during any endosonography approaches reviewed. The pooled sensitivities of endobronchial ultrasound-guided transbronchial needle aspiration and endoscopic ultrasound-guided fine-needle aspiration were 65% (95% confidence interval [CI], 52-76) and 73% (95% CI, 52-87), respectively, and specificities were 99% (95% CI, 78-100) and 99% (95% CI, 90-100), respectively. The area under the hierarchical summary receiver operating characteristic curves were 0.85 (95% CI, 0.81-0.88) for endobronchial ultrasound-guided transbronchial needle aspiration and 0.99 (95% CI, 0.98-1) for endoscopic ultrasound-guided fine-needle aspiration. Moreover, for patients who received chemotherapy alone, the pooled sensitivity of endosonography with lymph node sampling for restaging was 66% (95% CI, 56-75), and specificity was 100% (95% CI, 34-100); for patients who received chemoradiotherapy, the results seemed similar with a sensitivity of 77% (95% CI, 47-92) and specificity of 99% (95% CI, 48-100).CONCLUSIONS:Endosonography with lymph node sampling is an accurate and safe technique for mediastinal restaging of lung cancer.

This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Motivated by this observation, we propose a new holistic approach by 1) combining all similarity types used and 2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. We also discover "latent" features lost in the graph extraction process and integrate this into our framework. According to our experimental results, our holistic graph-based approach and its enhancement using corpus latent features are highly effective and our framework significantly outperforms previous approaches.

This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.

This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Inspired by this observation, in this paper, we propose a new holistic approach, by (1) combining all similarity types used and (2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. According to our experimental results, our holistic graph-based approach significantly outperforms previous approaches.

Tweets have become a comprehensive repository for real-time information. However, it is often hard for users to quickly get information they are interested in from tweets, owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView, an NLP-based tweet search platform to tackle this issue. Specifically, it exploits a series of natural language processing technologies, such as tweet normalization, named entity recognition, semantic role labeling, sentiment analysis, tweet classification, to extract useful information, i.e., named entities, events, opinions, etc., from a large volume of tweets. Then, non-noisy tweets, together with the mined information, are indexed, on top of which two brand new scenarios are enabled, i.e., categorized browsing and advanced search, allowing users to effectively access either the tweets or fine-grained information they are interested in.

Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-of-the-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the sentiment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we propose to improve target-dependent Twitter sentiment classification by 1) incorporating target-dependent features; and 2) taking related tweets into consideration. According to the experimental results, our approach greatly improves the performance of target-dependent sentiment classification.

This paper studies the problem of mining entity translation, specifically, mining English and Chinese name pairs. Existing efforts can be categorized into (a) a transliteration-based approach leveraging phonetic similarity and (b) a corpus-based approach exploiting bilingual co-occurrences, each of which suffers from inaccuracy and scarcity respectively. In clear contrast, we use unleveraged resources of monolingual entity co-occurrences, crawled from entity search engines, represented as two entity-relationship graphs extracted from two language corpora respectively. Our problem is then abstracted as finding correct mappings across two graphs. To achieve this goal, we propose a holistic approach, of exploiting both transliteration similarity and monolingual co-occurrences. This approach, building upon monolingual corpora, complements existing corpus-based work, requiring scarce resources of parallel or comparable corpus, while significantly boosting the accuracy of transliteration-based work. We validate our proposed system using real-life datasets.

Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.

We propose a novel MLN-based method that collectively conducts SRL on groups of news sentences. Our method is built upon a baseline SRL, which uses no parsers and leverages redundancy. We evaluate our method on a manually labeled news corpus and demonstrate that news redundancy significantly improves the performance of the baseline, e.g., it improves the F-score from 64.13% to 67.66%.

News tweets that report what is happening have become an important real-time information source. We raise the problem of Semantic Role Labeling (SRL) for news tweets, which is meaningful for fine grained information extraction and retrieval. We present a self-supervised learning approach to train a domain specific SRL system to resolve the problem. A large volume of training data is automatically labeled, by leveraging the existing SRL system on news domain and content similarity between news and news tweets. On a human annotated test set, our system achieves state-of-the-art performance, outperforming the SRL system trained on news.

Mining bilingual data (including bilingual sentences and terms) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed. Specifically, given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration; 3) pattern learning: learn generalized patterns with the identified seeds; 4) pattern based mining: extract all bilingual data in the page using the learned patterns. Our experiments on Chinese web pages produced more than 7.5 million pairs of bilingual sentences and more than 5 million pairs of bilingual terms, both with over 80% accuracy.

Part of the unique cultural heritage of China is the game of Chinese couplets (duìlián). One person challenges the other person with a sentence (first sentence). The other person then replies with a sentence (second sentence) equal in length and word segmentation, in a way that corresponding words in the two sentences match each other by obeying certain constraints on semantic, syntactic, and lexical relatedness. This task is viewed as a difficult problem in AI and has not been explored in the research community. In this paper, we regard this task as a kind of machine translation process. We present a phrase-based SMT approach to generate the second sentence. First, the system takes as input the first sentence, and generates as output an N-best list of proposed second sentences, using a phrase-based SMT decoder. Then, a set of filters is used to remove candidates violating linguistic constraints. Finally, a Ranking SVM is applied to rerank the candidates. A comprehensive evaluation, using both human judgments and BLEU scores, has been conducted, and the results demonstrate that this approach is very successful.

1 Mei Yang was an intern with MSR in the summer of 2007 The system combination approach combining system outputs at the word level is similar to the one described in (Rosti et al., 2007). Compared to the previous work, we developed a new method to generate a better alignment between multiple MT hypotheses from different individual systems, which is used to construct a high-quality confusion network. The details of our method will be elaborated in a future paper (He et al., 2008). First, a minimum Bayes risk (MBR) based method is used to select a backbone from the multiple hypotheses, then all the hypotheses are aligned to that backbone to form a confusion network, i.e., a word lattice in which each word is aligned to a list of alternative words (including null). Then, a set of features, including language model scores, word count, and normalized system voting score, are used to decode the confusion network. In training, a confusion network is constructed based on the multiple hypotheses of each sentence in a dev set. Then the corresponding feature weights are trained using Powell’s search to maximize the BLEU score on that dev set. In testing, a confusion network for each sentence in the test set is constructed and these feature weights are applied to decode the final MT output from the confusion network. In this entry, two language models are used, including a 3-gram LM trained on the English part of the parallel training data, and a 5gram LM trained on the whole English Gigaword corpus using a scalable LM toolkit (Nguyen et al., 2007).

A chitosan-based electrode filled with silver nanoparticles (AgNPs) and glucose oxidase (GOD) was used as an enzyme electrode to investigate the effect of aging process of AgNPs on the GOD activity. Freshly prepared AgNPs inhibit the GOD activity, however, the inhibitory effect decreased with the increase of aging time. After aged for a period of time, AgNPs showed enhancement effect on the GOD activity. The effect of aging was studied by the measurements of Ag+ ions concentration, zeta (ζ) potential and X-ray photoelectron spectroscopy (XPS). And the results indicated that the concentration of Ag+ ions in the silver sol decreased during the aging period (i.e. Ag+ ions converted to more inert silver metal Ag0). The effect of AgNPs on the GOD activity can be changed by controlling the aging time of AgNPs. This research provides a new and simple approach to mediate AgNPs property, which is of great value in potential application of AgNPs in biosensors and nanoscale devices.