SUMMARY The discovery of long non-coding RNA (lncRNA) has dramatically altered our understanding of cancer. Here, we describe a comprehensive analysis of lncRNA alterations at transcriptional, genomic, and epigenetic levels in 5,037 human tumor specimens across 13 cancer types from the Cancer Genome Atlas (TCGA). Our results suggest that the expression and dysregulation of lncRNAs are highly cancer-type specific compared to protein-coding genes. Using the integrative data generated by this analysis, we present a clinically guided small interfering RNA screening strategy and a co-expression analysis approach to identify cancer driver lncRNAs and predict their functions. This provides a resource for investigating lncRNAs in cancer and lays the groundwork for the development of new diagnostics and treatments.

Multidrug resistance (MDR) occurs frequently after long-term chemotherapy, resulting in refractory cancer and tumor recurrence. Therefore, combatting MDR is an important issue. Autophagy, a self-degradative system, universally arises during the treatment of sensitive and MDR cancer. Autophagy can be a double-edged sword for MDR tumors: it participates in the development of MDR and protects cancer cells from chemotherapeutics but can also kill MDR cancer cells in which apoptosis pathways are inactive. Autophagy induced by anticancer drugs could also activate apoptosis signaling pathways in MDR cells, facilitating MDR reversal. Therefore, research on the regulation of autophagy to combat MDR is expanding and is becoming increasingly important. We summarize advanced studies of autophagy in MDR tumors, including the variable role of autophagy in MDR cancer cells.

Summary In a genome-wide survey on somatic copy number alterations (SCNAs) of long non-coding RNA (lncRNA) in 2,394 tumor specimens from 12 cancer types, we found that about 21.8% of lncRNA genes were located in regions with focal SCNAs. By integrating bioinformatics analyses of lncRNA SCNAs and expression with functional screening assays, we identified an oncogene, Focally Amplified lncRNA on Chromosome 1 (FAL1), whose copy number and expression are correlated with outcomes in ovarian cancer. FAL1 associates with the epigenetic repressor BMI1 and regulates its stability in order to modulate the transcription of a number of genes including CDKN1A. The oncogenic activity of FAL1 is partially attributable to its repression of p21. FAL1-specific siRNAs significantly inhibit tumor growth in vivo.

ObjectivesDetection of dementia is essential for improving the lives of patients but the extent of underdetection worldwide and its causes are not known. This study aimed to quantify the prevalence of undetected dementia and to examine its correlates.Methods/setting/participantsA systematic search was conducted until October 2016 for studies reporting the proportion of undetected dementia and/or its determinants in either the community or in residential care settings worldwide. Random-effects models calculated the pooled rate of undetected dementia and subgroup analyses were conducted to identify determinants of the variation.Primary and secondary outcome measuresThe outcome measures of interest were the prevalence and determinants of undetected dementia.Results23 studies were eligible for inclusion in this review. The pooled rate of undetected dementia was 61.7% (95% CI 55.0% to 68.0%). The rate of underdetection was higher in China and India (vs Europe and North America), in the community setting (vs residential/nursing care), age of <70 years, male gender and diagnosis by general practitioner. However, it was lower in the studies using Mini-Mental State Examination (MMSE) diagnosis criteria.ConclusionsThe prevalence of undetected dementia is high globally. Wide variations in detecting dementia need to be urgently examined, particularly in populations with low socioeconomic status. Efforts are required to reduce diagnostic inequality and to improve early diagnosis in the community.

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IR-Net aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of outof-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.

Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and APIs as bags-of-words and lack a deep understanding of the semantics of the query.We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query. Instead of a bag-of-words assumption, it learns the sequence of words in a query and the sequence of associated APIs. DeepAPI adapts a neural language model named RNN Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length context vector, and generates an API sequence based on the context vector. We also augment the RNN Encoder-Decoder by considering the importance of individual APIs. We empirically evaluate our approach with more than 7 million annotated code snippets collected from GitHub. The results show that our approach generates largely accurate API sequences and outperforms the related approaches.

of a thesis submitted in partial fulfilment of the requirements for the Degree of M. C. M. Customer Switching Behaviour in the Chinese Retail Banking Industry By Dongmei ZhangWith the intense competition and increasing globalization in the financial markets, bank management must develop customer-oriented strategies in order to compete successfully in the competitive retail banking environment. The longer a bank can retain a customer, the greater revenue and cost savings from that customer. However, customers are also more prone to changing their banking behaviour when they can purchase nearly identical financial products provided by the retail banks. In order to stay competitive, bank managers need to understand the factors that influence and determine consumer's bank switching behaviour. In general, the results of this research allow service marketers and practitioners to develop and implement services marketing strategies to decrease customer defection rates, and in turn, increase bank profits. Furthermore, this research provides useful information for future researchers who study switching-behaviour in the banking industry.

New strategies that can simultaneously detect and remove highly toxic environmental pollutants such as heavy metal ions are still in urgent need. Herein, through supramolecular host–guest interactions, a fluorescent supramolecular polymer has been facilely constructed from a newly designed [2]­biphenyl-extended pillar[6]­arene equipped with two thymine sites as arms (H) and a tetraphenylethylene (TPE)-bridged bis­(quaternary ammonium) guest (G) with aggregation-induced emission (AIE) property. Interestingly, supramolecular assembly-induced emission enhancement (SAIEE) could be switched on upon addition of Hg2+ into the above-mentioned supramolecular polymer system to generate spherical-like supramolecular nanoparticles, due to the restriction of intramolecular rotation (RIR)-related AIE feature of G. Significantly, this supramolecular polymer with integrated modalities has been successfully used for real-time detection and removal of toxic heavy metal Hg2+ ions from water with quick response, high selectivity, and rapid adsorption rates, which could be efficiently regenerated and recycled without any loss via a simple treatment with Na2S. The newly developed supramolecular polymer system combines the inherent rigid and spacious cavity of novel extended-pillarene host with the AIE characteristics of TPE-based guest, suggesting a great potential in the treatment of heavy metal pollution and environmental sustainability.