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

Antibacterial dimethylaminododecyl methacrylate (DMADDM) was recently synthesized. The objectives of this study were to:(1) investigate antibacterial activity of DMADDM-containing primer on Streptococcus mutans impregnated into dentin blocks for the first time, and (2) compare the antibacterial efficacy of DMADDM with a previous quaternary ammonium dimethacrylate (QADM). Scotchbond Multi-Purpose (SBMP) bonding agent was used. DMADDM and QADM were mixed into SBMP primer. Six primers were tested:SBMP control primer P, P+2.5%DMADDM, P+5%DMADDM, P+7.5%DMADDM, P+10%DMADDM, and P+10%QADM. S. mutans were impregnated into human dentin blocks, and each primer was applied to dentin to test its ability to kill bacteria in dentinal tubules. Bacteria in dentin were collected via a sonication method, and the colony-forming units (CFU) and inhibition zones were measured. The bacterial inhibition zone of P+10%DMADDM was 10 times that of control primer (Po0.05). CFU in dentin with P+10%DMADDM was reduced by three orders of magnitude, compared with control. DMADDM had a much stronger antibacterial effect than QADM, and antibacterial efficacy increased with increasing DMADDM concentration. Dentin shear bond strengths were similar among all groups (P40.1). In conclusion, antibacterial DMADDM-containing primer was validated to kill bacteria inside dentin blocks, possessing a much stronger antibacterial potency than the previous QADM. DMADDM-containing bonding agent was effective in eradicating bacteria in dentin, and its efficacy was directly proportional to DMADDM mass fraction. Therefore, DMADDM may be promising for use in bonding agents as well as in other restorative and preventive materials to inhibit bacteria.