Spontaneous rhythmic activity of pacemaker neurons in the central nervous system underlies fundamental neurological processes such as locomotion, cognition and circadian rhythm. Among the wide range of ion channels required for its generation, the Ca2+-activated K+(KCa) channels play a prominent role in maintaining physiologically-relevant frequency and pattern of pacemaker activity. Much of our understanding of the functions of KCa channels in pacemaker neurons have been derived from pharmacological studies using channel modulators, such as iberiotoxin and apamin. Despite the significant advances made, recent studies have painted an increasingly complex picture of the effects of widely used KCa channel modulators on unintended targets that may confound our under?standing of their functions. In this review, we discussed the utility and shortcomings of the KCa channel modulators, and highlighted the significance of these findings, because the KCa channel modulators have been used in early clinical trials to treat disorders ranging from Parkinson disease to alcoholism.

In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.
Linguistics ; Machine Learning ; Natural Language Processing