MOTIVATION: Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. RESULTS: We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. AVAILABILITY AND IMPLEMENTATION: Newt's source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.

Building one's vocabulary in a language is an important component of language acquisition. Children learn their native language by being immersed in the language that is used in their environment. However, in second language acquisition, learners are often exposed to vocabulary that is selected by others specifically to aid language acquisition such as textbooks and word-lists. In this paper, we are presenting a machine learning based method for automatically selecting words that are relevant to the language acquisition task. The word relevancy is determined using data collected from 30 practicing English as a Second Language teachers for this purpose. We demonstrate the viability of this approach by using words from two major corpora, although in practice any corpora such as Google Books corpus can be utilized.