Voltage-dependent gating and block by internal spermine of the murine inwardly rectifying K+ channel, Kir2.1

The mechanism of inward rectification was investigated by recording single-channel currents through an inwardly rectifying K+ channel (Kir2.1). cDNA encoding a wild-type (WT) channel, a mutant replacing Asp 172 with Asn (D172N), and a tandem tetramer WT-(D172N)2-WT, was transfected into COS-1 cells using the liposome method, and after 48–72 h single-channel currents were recorded in the inside-out configuration at 150 mm internal and external K+. Steady-state open probability of outward currents decreased with larger depolarizations. The activation curve was fitted with a single Boltzmann equation. The voltages of half-activation in the absence of spermine were +35.9 mV (WT), +55.0 mV (WT-(D172N)2-WT) and +76.7 mV (D172N). Open-time and zero-current-time histograms were constructed. The open-time histogram was fitted with a single exponential function. Two exponential functions were necessary to fit the closed-time histogram. In each channel, internal spermine at a concentration of 1–100 nm reduced the open time of the outward currents in a concentration-dependent manner and produced one blocked state without affecting the inward currents, suggesting that spermine acts as an open channel blocker. The normalized steady-state open probability-spermine concentration curve was fitted by saturation kinetics with a Hill coefficient of 1. On the assumption of the linear sequential state model, the unblock and blocking rates were estimated in each channel. Unblock rates depended on the number of D172N mutant subunits, but blocking rates did not. The results suggest that closing gates work independently of the spermine block and D172 is involved in both intrinsic gating and the spermine block

Meta-Learning for Fast Incremental Learning

Abstract. Model based learning systems usually face to a problem of forgetting as a result of the incremental learning of new instances. Normally, the systems have to re-learn past instances to avoid this problem. However, the re-learning process wastes substantial learning time. To reduce learning time, we propose a novel incremental learning system, which consists of two neural networks: a main-learning module and a meta-learning module. The main-learning module approximates a continuous function between input and desired output value, while the meta-learning module predicts an appropriate change in parameters of the main-learning module for incremental learning. The meta-learning module acquires the learning strategy for modifying current parameters not only to adjust the main-learning module’s behavior for new instances but also to avoid forgetting past learned skills.

Mangifera indica as Bioindicator of Mercury Atmospheric Contamination in an ASGM Area in North Gorontalo Regency, Indonesia

We report the atmospheric Hg contamination in an artisanal and small-scale gold mining (ASGM) area in North Gorontalo, Indonesia. It is well known that atmospheric Hg contaminates the air, water, soil, and living organisms, including trees. In this study, we calculated total weight of heavy metals, especially Hg, and quantitatively measure the concentrations of heavy metals, especially Hg, in tree bark from an ASGM area. Tree bark can be used for the environmental assessment of atmospheric contamination because it attaches and absorbs heavy metals. Atmospheric Hg and other heavy metals, including Fe and Mn, and As were detected on the tree bark samples. The total weight of Hg, As, Fe, and Mn in the tree bark samples ranged from undetectable (ND) to 9.77, ND to 81.3, 124–4028, 37.0–1376 µg dry weight (DW), respectively per weight of sample. Based on quantitatively analysis micro-PIXE, the highest concentrations of all these metals were detected in the outer part of the bark. We conclude that tree bark can adsorb atmospheric contamination, which is then absorbed into the inner tissues