Developmental Expression of Kv Potassium Channels at the Axon Initial Segment of Cultured Hippocampal Neurons

Axonal outgrowth and the formation of the axon initial segment (AIS) are early events in the acquisition of neuronal polarity. The AIS is characterized by a high concentration of voltage-dependent sodium and potassium channels. However, the specific ion channel subunits present and their precise localization in this axonal subdomain vary both during development and among the types of neurons, probably determining their firing characteristics in response to stimulation. Here, we characterize the developmental expression of different subfamilies of voltage-gated potassium channels in the AISs of cultured mouse hippocampal neurons, including subunits Kv1.2, Kv2.2 and Kv7.2. In contrast to the early appearance of voltage-gated sodium channels and the Kv7.2 subunit at the AIS, Kv1.2 and Kv2.2 subunits were tethered at the AIS only after 10 days in vitro. Interestingly, we observed different patterns of Kv1.2 and Kv2.2 subunit expression, with each confined to distinct neuronal populations. The accumulation of Kv1.2 and Kv2.2 subunits at the AIS was dependent on ankyrin G tethering, it was not affected by disruption of the actin cytoskeleton and it was resistant to detergent extraction, as described previously for other AIS proteins. This distribution of potassium channels in the AIS further emphasizes the heterogeneity of this structure in different neuronal populations, as proposed previously, and suggests corresponding differences in action potential regulation

The Cycad Genotoxin MAM Modulates Brain Cellular Pathways Involved in Neurodegenerative Disease and Cancer in a DNA Damage-Linked Manner

Methylazoxymethanol (MAM), the genotoxic metabolite of the cycad azoxyglucoside cycasin, induces genetic alterations in bacteria, yeast, plants, insects and mammalian cells, but adult nerve cells are thought to be unaffected. We show that the brains of adult C57BL6 wild-type mice treated with a single systemic dose of MAM acetate display DNA damage (O6-methyldeoxyguanosine lesions, O6-mG) that remains constant up to 7 days post-treatment. By contrast, MAM-treated mice lacking a functional gene encoding the DNA repair enzyme O6-mG DNA methyltransferase (MGMT) showed elevated O6-mG DNA damage starting at 48 hours post-treatment. The DNA damage was linked to changes in the expression of genes in cell-signaling pathways associated with cancer, human neurodegenerative disease, and neurodevelopmental disorders. These data are consistent with the established developmental neurotoxic and carcinogenic properties of MAM in rodents. They also support the hypothesis that early-life exposure to MAM-glucoside (cycasin) has an etiological association with a declining, prototypical neurodegenerative disease seen in Guam, Japan, and New Guinea populations that formerly used the neurotoxic cycad plant for food or medicine, or both. These findings suggest environmental genotoxins, specifically MAM, target common pathways involved in neurodegeneration and cancer, the outcome depending on whether the cell can divide (cancer) or not (neurodegeneration). Exposure to MAM-related environmental genotoxins may have relevance to the etiology of related tauopathies, notably, Alzheimer's disease

ACK-less rate adaptation using distributional reinforcement learning for reliable IEEE 802.11bc broadcast WLANs

Abstract As a step towards establishing reliable broadcast wireless local area networks (WLANs), this paper proposes acknowledgement (ACK)-less rate adaptation to alleviate reception failures at broadcast recipient stations (STAs) using distributional reinforcement learning (RL). The key point of this study is that the algorithms for learning the strategy of ACK-less rate adaptation are evaluated in terms of the broadcast performance, which is composed of the data rate of the broadcast access point (AP) and the reception success rate at the recipient STAs. ACK-less rate adaptation framework was realized using the received signal strength (RSS) of the uplink frames transmitted by the non-broadcast STAs to the non-broadcast APs, which correlated with the broadcast performance with a confounding effect from the deployment of the broadcast recipient STAs. However, this rate adaptation framework has the problem that it incurs the reception failures at a part of the broadcast recipient STAs, because deep Q-learning used in the previous framework cannot deal with the wide distribution of the broadcast performance. To address this challenge, this paper further discusses the rate adaptation using distributional RL, which approximates the entire distribution of the broadcast performance. The simulations confirmed the following: 1) Using the expected broadcast performance learned by deep Q-learning improved the performance in terms of the Pareto efficiency. 2) Learning the entire distribution of the broadcast performance enabled the broadcast AP to determine the tail of the distribution using risk measure, and to alleviate reception failures while implementing the rate adaptation in the same way as the method that learns only expected broadcast performance

Interference-free AP identification and shared information reduction for tabular Q-learning-based WLAN coordinated spatial reuse

Abstract Access point (AP) coordinated spatial reuse with Q-learning enables efficient spectrum utilization [1]. Although sharing of transmission schedules among APs is necessary for coordination, there is no mechanism to identify the APs with which the schedules are to be shared, resulting in excess information being shared among APs. In this study, we propose a scheme to identify the interference-free APs that are not required for sharing of information by comparing Q-values. A simple simulation demonstrates that this scheme successfully reduces shared information without throughput degradation