A hybrid diagnosis model for determining the types of the liver disease

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.     

Gaussian source model based iterative algorithm for EEG source imaging

Estimation of the neural active sources from the scalp electroencephalogram (EEG) is an ill-posed inverse problem. In this paper, we propose a new source model: Gaussian distributed Source Model (GSM), to model the activations in brain. GSM may imitate an Isolated Source Model (ISM) or a Distributed Source Model (DSM) by adopting different supporting range parameter of the Gaussian function. Using GSM, an iterative Gaussian source Imaging Algorithm (GIA) is developed to detect the EEG sources. As GIA dynamically reduces the solution space, the solution may gradually converge to a desired distribution. A comparative evaluation among LORETA, FOCUSS and GIA was conducted for both isolated point sources and distributed sources, the results demonstrate that GIA is more flexible and efficient for various actual sources configurations. Finally, GSM was applied to real recordings obtained from a visual spatial attention task; the corresponding source activation areas of the early component are localized in contralateral occipital cortices, consistent with the retinotopic organization of early visual spatial attention effects.     

Study on the oxidation form of adenine in phosphate buffer solution

The oxidation of adenine in phosphate buffer solution is investigated using square-wave voltammetry and in situ UV spectroelectrochemistry. The geometry of adenine and the derivatives optimized at DFTB3LYP-6-31G (d, p)-PCM level is in agreement with the crystal structure, and the imitated UV spectra of adenine and the product at electrode are consistent with the in situ UV spectra. The relationship between the electrochemical property and the molecular structure is also discussed. The experimental and theoretical results show that the adenine oxidation origins from the neutral adenine.     

Analysis and recognition of touching cell images based on morphological structures

Automated analysis and recognition of cell-nuclear phases using fluorescence microscopy images play an important role for high-content screening. A major task of automated imaging based high-content screening is to segment and reconstruct each cell from the touching cell images. In this paper we present new useful method for recognizing morphological structural models of touching cells, detecting segmentation points, determining the number of segmented cells in touching cell image, finding the related data of segmented cell arcs and reconstructing segmented cells. The conceptual frameworks are based on the morphological structures where a series of structural points and their morphological relationships are established. Experiment results have shown the efficient application of the new method for analysis and recognition of touching cell images of high-content screening.     

Ensembled support vector machines for human papillomavirus risk type prediction from protein secondary structures

Infection by the human papillomavirus (HPV) is regarded as the major risk factor in the development of cervical cancer. Detection of high-risk HPV is important for understanding its oncogenic mechanisms and for developing novel clinical tools for its diagnosis, treatment, and prevention. Several methods are available to predict the risk types for HPV protein sequences. Nevertheless, no tools can achieve a universally good performance for all domains, including HPV and nor do they provide confidence levels for their decisions. Here, we describe ensembled support vector machines (SVMs) to classify HPV risk types, which assign given proteins into high-, possibly high-, or low-risk type based on their confidence level. Our approach uses protein secondary structures to obtain the differential contribution of subsequences for the risk type, and SVM classifiers are combined with a simple but efficient string kernel to handle HPV protein sequences. In the experiments, we compare our approach with previous methods in accuracy and F1-score, and present the predictions for unknown HPV types, which provides promising results.     

Medical Student Dermatology Interest Groups

The Dermatology Interest Group (DIG) at the University of Texas Medical Branch (UTMB) blog (digutmb.blogspot.com) was created in 2004 with the aims of increasing communication and collaboration among students, faculty, residents, and alumni, promoting educational opportunities, and fostering the missions for which DIG was created. This blog is unique, because its frequent activity is directed toward the educational and professional needs of medical students and residents. We assessed the use of this blog by evaluating the number of blog views and audience members with relationship to the number of posts and post content over time via a tracking system. We found that there has been an increase in blog posts, views, and subscribers, as well as in areas of post content including dermatology resources/news/articles, residency applications, and resident-related information. Usefulness of such posts expands beyond UTMB students, which increases blog views and widens viewer audience. An international viewer population also was evaluated. Recorded blog viewing time was 1 minute, 57 seconds, which is more time than needed to read a post, suggesting use of additional blog information. This review of the DIG at the UTMB blog demonstrates how the use of web-based tools, in addition to the inherent benefits of medical student interests groups, are valuable resources for students, residents, and faculty.