BackgroundHypnotics have been reported to be associated with dementia. However, the relationship between insomnia, hypnotics and dementia is still controversial. We sought to examine the risk of dementia in patients with long-term insomnia and the contribution of hypnotics.MethodsData was collected from Taiwan’s Longitudinal Health Insurance Database. The study cohort comprised all patients aged 50 years or older with a first diagnosis of insomnia from 2002 to 2007. The comparison cohort consisted of randomly selected patients matched by age and gender. Each patient was individually tracked for 3 years from their insomnia index date to identify whether the patient had a first diagnosis of dementia. Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsWe identified 5693 subjects with long-term insomnia and 28,465 individuals without. After adjusting for hypertension, diabetes mellitus, hyperlipidemia, and stroke, those with long-term insomnia had significantly higher risks of dementia (HR, 2.34; 95% CI, 1.92–2.85). Patients with long-term insomnia and aged 50 to 65 years had a higher increased risk of dementia (HR, 5.22; 95% CI, 2.62–10.41) than those older than 65 years (HR, 2.33; 95% CI, 1.90–2.88). The use of hypnotics with a longer half-life and at a higher prescribed dose predicted a greater increased risk of dementia.ConclusionsPatients with long-term use of hypnotics have more than a 2-fold increased risk of dementia, especially those aged 50 to 65 years. In addition, the dosage and half-lives of the hypnotics used should be considered, because greater exposure to these medications leads to a higher risk of developing dementia.

This paper proposes an effective term suggestion approach to interactive Web search. Conventional approaches to making term suggestions involve extracting co-occurring keyterms from highly ranked retrieved documents. Such approaches must deal with term extraction difficulties and interference from irrelevant documents, and, more importantly, have difficulty extracting terms that are conceptually related but do not frequently co-occur in documents. In this paper, we present a new, effective log-based approach to relevant term extraction and term suggestion. Using this approach, the relevant terms suggested for a user query are those that cooccur in similar query sessions from search engine logs, rather than in the retrieved documents. In addition, the suggested terms in each interactive search step can be organized according to its relevance to the entire query session, rather than to the most recent single query as in conventional approaches. The proposed approach was tested using a proxy server log containing about two million query transactions submitted to search engines in Taiwan. The obtained experimental results show that the proposed approach can provide organized and highly relevant terms, and can exploit the contextual information in a user's query session to make more effective suggestions.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.

This is the first nationwide population-based epidemiological study of PCA. We demonstrate that PCA can be associated with other disorders, especially AD.

This article presents the design of a sequence-based predictor named ProteDNA for identifying the sequence-specific binding residues in a transcription factor (TF). Concerning protein–DNA interactions, there are two types of binding mechanisms involved, namely sequence-specific binding and nonspecific binding. Sequence-specific bindings occur between protein sidechains and nucleotide bases and correspond to sequence-specific recognition of genes. Therefore, sequence-specific bindings are essential for correct gene regulation. In this respect, ProteDNA is distinctive since it has been designed to identify sequence-specific binding residues. In order to accommodate users with different application needs, ProteDNA has been designed to operate under two modes, namely, the high-precision mode and the balanced mode. According to the experiments reported in this article, under the high-precision mode, ProteDNA has been able to deliver precision of 82.3%, specificity of 99.3%, sensitivity of 49.8% and accuracy of 96.5%. Meanwhile, under the balanced mode, ProteDNA has been able to deliver precision of 60.8%, specificity of 97.6%, sensitivity of 60.7% and accuracy of 95.4%. ProteDNA is available at the following websites:http://protedna.csbb.ntu.edu.tw/http://protedna.csie.ntu.edu.tw/http://bio222.esoe.ntu.edu.tw/ProteDNA/.

Background: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). Results: In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. Conclusions: The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

Supplementary data are available at Bioinformatics online.