An Edge Transfer Learning Approach for Calibrating Soil Electrical Conductivity Sensors.

Smart agriculture utilizes Internet of Things (IoT) technologies to enable low-cost electrical conductivity (EC) sensors to support farming intelligence. Due to aging and changes in weather and soil conditions, EC sensors are prone to long-term drift over years of operation. Therefore, regular recalibration is necessary to ensure data accuracy. In most existing solutions, an EC sensor is calibrated by using the standard sensor to build the calibration table. This paper proposes SensorTalk3, an ensemble approach of machine learning models including XGBOOST and Random Forest, which can be executed at an edge device (e.g., Raspberry Pi) without GPU acceleration. Our study indicates that the soil information (both temperature and moisture sensor data) plays an important role in SensorTalk3, which significantly outperforms the existing calibration approaches. The MAPE of SensorTalk3 can be as low as 1.738%, compared to the 7.792% error of the original sensor. Our study indicates that when the errors of uncalibrated moisture and temperature sensors are not larger than 8.3%, SensorTalk3 can accurately calibrate EC. SensorTalk3 can perform model training during data collection at the edge node. When all training data are collected, AI training is also finished at the edge node. Such an AI training approach has not been found in existing edge AI approaches. We also proposed the dual-sensor detection solution to determine when to conduct recalibration. The overhead of this solution is less than twice the optimal detection scenario (which cannot be achieved practically). If the two non-standard sensors are homogeneous and stable, then the optimal detection scenario can be approached. Conventional methods require training calibration AI models in the cloud. However, SensorTalk3 introduces a significant advancement by enabling on-site transfer learning in the edge node. Given the abundance of farming sensors deployed in the fields, performing local transfer learning using low-cost edge nodes proves to be a more cost-effective solution for farmers.

Gossypol, a novel modulator of VCP, induces autophagic degradation of mutant huntingtin by promoting the formation of VCP/p97-LC3-mHTT complex.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by toxic aggregates of mutant huntingtin protein (mHTT) in the brain. Decreasing mHTT is a potential strategy for therapeutic purpose of HD. Valosin-containing protein (VCP/p97) is a crucial regulator of proteostasis, which regulates the degradation of damaged protein through proteasome and autophagy pathway. Since VCP has been implicated in pathogenesis of HD as well as other neurodegenerative diseases, small molecules that specifically regulate the activity of VCP may be of therapeutic benefits for HD patients. In this study we established a high-throughput screening biochemical assay for VCP ATPase activity measurement and identified gossypol, a clinical approved drug in China, as a novel modulator of VCP. Gossypol acetate dose-dependently inhibited the enzymatic activity of VCP in vitro with IC50 of 6.53±0.6 µM. We further demonstrated that gossypol directly bound to the interface between the N and D1 domains of VCP. Gossypol acetate treatment not only lowered mHTT levels and rescued HD-relevant phenotypes in HD patient iPS-derived Q47 striatal neurons and HD knock-in mouse striatal cells, but also improved motor function deficits in both Drosophila and mouse HD models. Taken together, gossypol acetate acted through a gain-of-function way to induce the formation of VCP-LC3-mHTT ternary complex, triggering autophagic degradation of mHTT. This study reveals a new strategy for treatment of HD and raises the possibility that an existing drug can be repurposed as a new treatment of neurodegenerative diseases.

Inhibition of Na+ channel currents in rat myoblasts by 4-aminopyridine.

Our previous study revealed that 4-aminopyridine (4-AP), a specific blocker of A-type current, could also inhibit inward Na+ currents (I(Na)) with a state-independent mechanism in rat cerebellar granule cells. In the present study, we report an inhibitory effect of 4-AP on voltage-gated and tetrodotoxin (TTX)-sensitive I(Na) recorded from cultured rat myoblasts. 4-AP inhibited I(Na) amplitude in a dose-dependent manner between the concentrations of 0.5 and 10 mM without significant alteration in the activation or inactivation kinetics of the channel. By comparison to the 4-AP-induced inhibitory effect on cerebellum neurons, the inhibitory effect on myoblasts was enhanced through repetitive pulse and inflected by changing frequency. Specifically, the lower the frequency of pulse, the higher the inhibition observed, suggesting that block manner is inversely use-dependent. Moreover, experiments adding 4-AP to the intracellular solution indicate that the inhibitory effects are localized inside the cell. Additionally, 4-AP significantly modifies the properties of steady-state activation and inactivation kinetics of the channel. Our data suggest that the K+ channel blocker 4-AP inhibits both neuron and myoblast Na+ channels via different mechanisms. These findings may also provide information regarding 4-AP-induced pharmacological and toxicological effects in clinical use and experimental research.