scholarly journals Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution

2011 ◽  
Vol 105 (2) ◽  
pp. 964-980 ◽  
Author(s):  
Andrew Miri ◽  
Kayvon Daie ◽  
Rebecca D. Burdine ◽  
Emre Aksay ◽  
David W. Tank
Keyword(s):  
Time Series ◽  
Optical Imaging ◽  
Neural Activity ◽  
Laser Scanning ◽  
Large Scale ◽  
Neural Encoding ◽  
Quantitative Measurements ◽  
Cellular Resolution ◽  
Activity Measurements ◽  
General Regression

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.

scholarly journals Large-scale cellular-resolution imaging of neural activity in freely behaving mice

2021 ◽  
Author(s):  
D.P. Leman ◽  
I.A. Chen ◽  
K.A. Bolding ◽  
J. Tai ◽  
L.K. Wilmerding ◽  
...  
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
First Generation ◽  
Brain Regions ◽  
Field Of View ◽  
Cellular Resolution ◽  
Neural Populations ◽  
Resolution Imaging ◽  
Freely Behaving ◽  
Freely Moving

AbstractMiniaturized microscopes for head-mounted fluorescence imaging are powerful tools for visualizing neural activity during naturalistic behaviors, but the restricted field of view of first-generation ‘miniscopes’ limits the size of neural populations accessible for imaging. Here we describe a novel miniaturized mesoscope offering cellular-resolution imaging over areas spanning several millimeters in freely moving mice. This system enables comprehensive visualization of activity across entire brain regions or interactions across areas.

Towards synergy of GNSS and InSAR mining deformation monitoring with Sentinel-1 data

2021 ◽  
Author(s):  
Damian Tondaś ◽  
Maya Ilieva ◽  
Witold Rohm ◽  
Jan Kapłon
Keyword(s):  
Time Series ◽  
Real Time ◽  
Laser Scanning ◽  
Large Scale ◽  
Ground Deformation ◽  
Dimensional Space ◽  
Satellite Navigation ◽  
Deformation Monitoring ◽  
North East ◽  
The Impact

<p>The determination of ground deformation may be carried out by applying various measurement methods such as levelling, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR) and many others. In this work, we focus on the comparison of the deformation effects measured by Global Navigation Satellite Systems (GNSS) and satellite Interferometric SAR (InSAR) methods in the Upper-Silesian coal mining region (SW Poland).</p><p>An unquestionable advantage of GNSS technology is the possibility of continuous monitoring of deformations in three-dimensional space. Moreover, the evolution of real-time (RT) techniques such as: near real-time (NRT), ultra-fast NRT or RT allows to obtain a high precise position determination with a relatively slight latency (ranging from a few seconds to less than one hour). The limitation of the satellite navigation technology is the spatial range of the measurements. The deformation can only be observed at the point where the GNSS antenna is located. Furthermore, the acquisition, installation and maintenance of the equipment may also involve high costs.</p><p>In contrast to the GNSS technique, the InSAR methods enable measurement of the large-scale subsidence areas with possibility to use free products and software (e.g. Sentinel-1 and SNAP). The large-scale InSAR investigations provide a better overview of local terrain changes. Unfortunately, InSAR methods also have some limitations related to data acquisition technology:  </p><ul><li>a few days latency in acquiring a new image,</li> <li>insensitivity to changes in the northern component,</li> <li>acquiring deformation only in the LOS direction.</li> </ul><p>The main goal of this research is to analyse the deformation results obtained using GNSS and InSAR methods with respect to the capabilities and limitations of these two techniques. We investigated the level of agreement of eight GNSS and InSAR time series in areas with no subsidence, together with results acquired on seven regions of mining deformation where the maximum subsidence velocity exceeds 50 cm/year. The mean RMS time series fitting error obtained in subsidence basin is more than 5 cm and in non-deformed areas is equal to 2 cm. The study also investigated the effect of coherence threshold levels (0.3 ÷ 0.6) with introduction of the northern GNSS component on the InSAR decomposition process. Assuming the same GNSS deformation value in each directions (north, east, and up), the impact of the northern component was estimated as 10% of the total LOS subsidence.</p>

scholarly journals Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice

Neuron ◽  
2007 ◽  
Vol 56 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Daniel A. Dombeck ◽  
Anton N. Khabbaz ◽  
Forrest Collman ◽  
Thomas L. Adelman ◽  
David W. Tank
Keyword(s):  
Neural Activity ◽  
Large Scale ◽  
Cellular Resolution

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

scholarly journals Alien Wood Species as a Resource for Wood-Plastic Composites

2020 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Sergej Medved ◽  
Daša Krapež Tomec ◽  
Angela Balzano ◽  
Maks Merela
Keyword(s):  
Wood Species ◽  
Dimensional Stability ◽  
Laser Scanning ◽  
Large Scale ◽  
Laser Scanning Microscopy ◽  
Raw Material ◽  
Confocal Laser ◽  
Alien Plant ◽  
Wood Plastic Composites ◽  
Plastic Composites

Since invasive alien species are one of the main causes of biodiversity loss in the region and thus of changes in ecosystem services, it is important to find the best possible solution for their removal from nature and the best practice for their usability. The aim of the study was to investigate their properties as components of wood-plastic composites and to investigate the properties of the wood-plastic composites produced. The overall objective was to test the potential of available alien plant species as raw material for the manufacture of products. This would contribute to sustainability and give them a better chance of ending their life cycle. One of the possible solutions on a large scale is to use alien wood species for the production of wood plastic composites (WPC). Five invasive alien hardwood species have been used in combination with polyethylene powder (PE) and maleic anhydride grafted polyethylene (MAPE) to produce various flat pressed WPC boards. Microstructural analyses (confocal laser scanning microscopy and scanning electron microscopy) and mechanical tests (flexural strength, tensile strength) were performed. Furthermore, measurements of density, thickness swelling, water absorption and dimensional stability during heating and cooling were carried out. Comparisons were made between the properties of six WPC boards (five alien wood species and mixed boards). The results showed that the differences between different invasive alien wood species were less obvious in mechanical properties, while the differences in sorption properties and dimensional stability were more significant. The analyses of the WPC structure showed a good penetration of the polymer into the lumens of the wood cells and a fine internal structure without voids. These are crucial conditions to obtain a good, mechanically strong and water-resistant material.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

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