F61 Digital, high-frequency, long-term monitoring of motor and non-motor symptoms in huntington’s disease (hd) patients

Glutamate is the main excitatory neurotransmitter in the brain, and impairments in its signaling are associated with many neurological disorders, including Huntington’s disease (HD). Previous studies in HD mouse models demonstrate altered glutamate receptor distribution and signaling at cortico-striatal synapses, and some studies suggest that glutamate release is altered; however, traditional methods to study synaptic glutamate release are indirect or have poor temporal resolution. Here we utilize iGluSnFR, a modified green fluorescent protein reporter for real-time imaging of glutamate transmission, to study presynaptic modulation of cortical glutamate release in the striatum of the YAC128 HD mouse model. We determined that iGluSnFR can be used to accurately measure short- and long-term changes in glutamate release caused by modulation of extracellular Ca2+ levels, activation of presynaptic receptors, and high-frequency stimulation (HFS) protocols. We also confirmed a difference in the expression of HFS-induced long-term depression in YAC128. Together, this research demonstrates the utility of iGluSnFR in studying presynaptic modulation of glutamate release in healthy mice and disease models that display impairments in glutamate signaling. NEW & NOTEWORTHY We use iGluSnFR to directly assess presynaptic modulation of cortico-striatal glutamate release in brain slice and compare changes in glutamate release between wild type and a Huntington’s disease mouse model, YAC128. We observed reductions in glutamate release after low extracellular Ca2+ and activation of various presynaptic receptors. We also demonstrate a presynaptic mechanism of reduced glutamate release in high-frequency stimulation-induced long-term depression and show this to be altered in YAC128.

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High‐frequency Acoustic Recording Package (HARP) for long‐term monitoring of marine mammals

The Journal of the Acoustical Society of America ◽

10.1121/1.4787068 ◽

2006 ◽

Vol 120 (5) ◽

pp. 3015-3015

Author(s):

Sean M. Wiggins ◽

Chris Garsha ◽

Greg Campbell ◽

John A. Hildebrand

Keyword(s):

High Frequency ◽

Marine Mammals ◽

Long Term Monitoring ◽

Acoustic Recording ◽

Term Monitoring

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The Italian TREETALKER NETWORK (ITT-Net): continuous large scale monitoring of tree functional traits and vulnerabilities to climate change

10.5194/egusphere-egu2020-20591 ◽

2020 ◽

Author(s):

Simona Castaldi ◽

Serena Antonucci ◽

Shahla Asgharina ◽

Giovanna Battipaglia ◽

Luca Belelli Marchesini ◽

...

Keyword(s):

Climate Change ◽

Real Time ◽

High Frequency ◽

Large Scale ◽

Operation Mode ◽

Cloud Services ◽

Long Term Monitoring ◽

The Impact ◽

Term Monitoring

The &#160;Italian TREETALKER NETWORK (ITT-Net) aims to respond to one of the grand societal challenges: the impact of climate changes on forests ecosystem services and forest dieback. The comprehension of the link between these phenomena requires to complement the most classical approaches with a new monitoring paradigm based on large scale, single tree, high frequency and long-term monitoring tree physiology, which, at present, is limited by the still elevated costs of multi-sensor devices, their energy demand and maintenance not always suitable for monitoring in remote areas. The ITT-Net network will be a unique and unprecedented worldwide example of real time, large scale, high frequency and long-term monitoring of tree physiological parameters. By spring 2020, as part of a national funded project (PRIN) the network will have set 37 sites from the north-east Alps to Sicily where a new low cost, multisensor technology &#8220;the TreeTalker&#174;&#8221; equipped to measure tree radial growth, sap flow, transmitted light spectral components related to foliage dieback and physiology and plant stability (developed by Nature 4.0), will monitor over 600 individual trees. A radio LoRa protocol for data transmission and access to cloud services will allow to transmit in real time high frequency data on the WEB cloud with a unique IoT identifier to a common database where big data analysis will be performed to explore the causal dependency of climate events and environmental disturbances with tree functionality and resilience.With this new network, we aim to create a new knowledge, introducing a massive data observation and analysis, about the frequency, intensity and dynamical patterns of climate anomalies perturbation on plant physiological response dynamics in order to: 1) characterize the space of &#8220;normal or safe tree operation mode&#8221; during average climatic conditions; 2) identify the non-linear tree responses beyond the safe operation mode, induced by extreme events, and the tipping points; 3) test the possibility to use a high frequency continuous monitoring system to identify early warning signals of tree stress which might allow to follow tree dynamics under climate change in real time at a resolution and accuracy that cannot always be provided through forest inventories or remote sensing technologies.To have an overview of the ITT Network you can visit www.globaltreetalker.org&#160;

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A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine

Remote Sensing ◽

10.3390/rs11161891 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1891 ◽

Cited By ~ 3

Author(s):

Hanzeyu Xu ◽

Yuchun Wei ◽

Chong Liu ◽

Xiao Li ◽

Hong Fang

Keyword(s):

High Frequency ◽

Impervious Surface ◽

Google Earth ◽

Impervious Surfaces ◽

Surface Dynamics ◽

Google Earth Engine ◽

Long Term Monitoring ◽

Disturbance And Recovery ◽

Term Monitoring

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

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