Resizable, Rescalable and Free-Style Visualization of Hierarchical Clustering and Bioinformatics Analysis

Graphical representation of hierarchical clustering results is of final importance in hierarchical cluster analysis of data. Unfortunately, almost all mathematical or statistical software may have a weak capability of showcasing such clustering results. Particularly, most of clustering results or trees drawn cannot be represented in a dendrogram with a resizable, rescalable and free-style fashion. With the “dynamic” drawing instead of “static” one, this research works around these weak functionalities that restrict visualization of clustering results in an arbitrary manner. It introduces an algorithmic solution to these functionalities, which adopts seamless pixel rearrangements to be able to resize and rescale dendrograms or tree diagrams. The results showed that the algorithm developed makes clustering outcome representation a really free visualization of hierarchical clustering and bioinformatics analysis. Especially, it possesses features of selectively visualizing and/or saving results in a specific size, scale and style (different views).

A bibliometric analysis of research on nursing models in countries other than China

Objectives＂ To deepen our understanding of the status quo and to identify the hot topics and develop- mental trends of research on nursing models in countries other than China in the most recent decade. Methods： The study subjects were the publications retrieved from the PubMed database using the MeSH terms of ＂Models, Nursing＂. Bibliographic item co-occurrence mining system （BICOMS） software was used for conventional bibliometric analysis of publications during two time periods, 2005-2009 and 2010-2014. The number of published journal articles, journal distribution, authors of publications, country of origin of journals, and language of publications were analyzed to establish a high-frequency keyword profile and co-occurrence matrix. Graphical clustering toolkit （gCLUTO） software was applied for two-way clustering analysis and visualized analysis. Results： A total of 1472 journal articles with a key theme of nursing models were retrieved for final analysis, including 771 published during 2005-2009 and 701 during 2010-2014. The bibliometric analysis revealed that publications other than China concerning nursing models were mostly concentrated in the United States and the United Kingdom and that the number of relevant publications has been continuously decreasing. The two-way clustering analysis showed that there were mainly four types of research themes in the relevant publications in countries other than China during 2005-2009, i.e., nursing education and theoretical research, clinical nursing and psychological care, nursing administration, and models of nursing education, whereas there were five types during 2010-2014, i.e., nursing theories and clinical nursing practice, nursing administration models and assessments of nurses＇ knowledge and skills, community nursing administration models, nursing human resource management, and nursing education models and approaches. Conclusions： Research on nursing models in countries other than China is relatively mature and stable with a broader view, but it has shown a declining trend in recent years. It emphasizes both theory and practice, with research content tending to be structured into four modules, i.e., nursing education, administration, clinical practice, and theoretical research. Community nursing models may become a key research direction in the international research on nursing models in the future.

Visual interactive image clustering:a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.