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Interval-censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval. In this paper, we propose a semiparametric probit model for analyzing case 2 interval-censored data as an alternative to the existing semiparametric models in the literature. Specifically, we propose to approximate the unknown nonparametric nondecreasing function in the probit model with a linear combination of monotone splines, leading to only a finite number of parameters to estimate. Both the maximum likelihood and the Bayesian estimation methods are proposed. For each method, regression parameters and the baseline survival function are estimated jointly. The proposed methods make no assumptions about the observation process and can be applicable to any interval-censored data with easy implementation. The methods are evaluated by simulation studies and are illustrated by two real-life interval-censored data applications.

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Gliomas are the most common form of primary brain tumor in the adult central nervous system. Altered expression and prognostic value of transmembrane protein 97 (TMEM97) has been recently reported in different types of human tumors. However, the association of TMEM97and glioma is poorly defined. Here, we reported that TMEM97 was significantly increased in glioma tissues compared to non-tumorous brain tissues. Furthermore, TMEM97 levels were progressively increased with increasing histologic tumor grade in glioma. Higher TMEM97 expression level was correlated with shorter survival time of patients with glioma. Downregulation of TMEM97 through RNA interference inhibited cell proliferation and G1/S transition in two glioma cell lines, U87 and U373. More importantly, TMEM97 silencing induced a significant decrease in the expression of G1/S transition key regulators, cyclin D1, cyclin E, CDK2, and CDK4. Additionally, downregulation of TMEM97 in glioma cells notably repressed cell migration and cell invasion. Further analysis suggested that the decreased invasion was associated with alterations in EMT markers, including E-cadherin, β-catenin, and Twist. Since expression of TMEM97 seems to be associated with the oncogenic potential of glioma, and suppression of its expression can inhibit cancer cell growth and metastasis, TMEM97 may be a potential therapeutic target in human glioma.

The semantics of mathematical formulae depend on their spatial structure, and they usually exist in layout presentations such as PDF, L A T E X, and Presentation MathML, which challenges previous text index and retrieval methods. This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF documents. Unlike prior studies, which require users to manually input formula markup language as query, the new system enables users to "copy" formula queries directly from PDF documents. Furthermore, by using a novel indexing and matching model, the system is aimed at searching for similar mathematical formulae based on both textual and spatial similarities. A hierarchical generalization technique is proposed to generate sub-trees from the semi-operator tree of formulae and support substructure match and fuzzy match. Experiments based on massive Wikipedia and CiteSeer repositories show that the new system along with novel algorithms, comparing with two representative mathematics retrieval systems, provides more efficient mathematical formula index and retrieval, while simplifying user query input for PDF documents.

Our data offer the convincing evidence for the first time that serum miR-335 level may be markedly and consistently increased in pediatric AML patients. Serum miR-335 may serve as a promising marker for monitoring the progression and predicting the clinical outcome of patients with this disease.

As a special type of table understanding, the detection and analysis of tables of contents (TOCs) play an important role in the digitization of multi-page documents. Most previous TOC analysis methods only concentrate on the TOC itself without taking into account the other pages in the same document. Besides, they often require manual coding or at least machine learning of document-specific models. This paper introduces a new method to detect and analyze TOCs based on content association. It fully leverages the text information throughout the whole multi-page document and can be directly applied to a wide range of documents without the need to build or learn the models for individual documents. In addition, the associations of general text and page numbers are combined to make the TOC analysis more accurate. Natural language processing and layout analysis are integrated to improve the TOC functional tagging. The applications of the proposed method in a large-scale digital library project are also discussed.