scholarly journals Taste sensing and sugar detection mechanisms in Drosophila larval primary taste center

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
G Larisa Maier ◽  
Nikita Komarov ◽  
Felix Meyenhofer ◽  
Jae Young Kwon ◽  
Simon G Sprecher
Keyword(s):  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Single Neurons ◽  
Sugar Sensing ◽  
Wide Range ◽  
Gustatory Sense ◽  
Detection Mechanisms

Despite the small number of gustatory sense neurons, Drosophila larvae are able to sense a wide range of chemicals. Although evidence for taste multimodality has been provided in single neurons, an overview of gustatory responses at the periphery is missing and hereby we explore whole-organ calcium imaging of the external taste center. We find that neurons can be activated by different combinations of taste modalities including of opposite hedonic valence and identify distinct temporal dynamics of response. Although sweet sensing has not been fully characterized so far in the external larval gustatory organ, we recorded responses elicited by sugar. Previous findings established that larval sugar sensing relies on the Gr43a pharyngeal receptor, but the question remains if external neurons contribute to this taste. Here we postulate that external and internal gustation use distinct and complementary mechanisms in sugar sensing and we identify external sucrose sensing neurons.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

Development of innovative monitoring technologies in the framework of InnovaMare Project

2021 ◽  
Author(s):  
Angelo Odetti ◽  
Federica Braga ◽  
Fabio Brunetti ◽  
Massimo Caccia ◽  
Simone Marini ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Shallow Water ◽  
Adriatic Sea ◽  
Temporal Dynamics ◽  
Supervised Machine Learning ◽  
Detection Techniques ◽  
Cross Border ◽  
Wide Range ◽  
Air Cushion ◽  
The Cross

<p>The IT-HR InnovaMare project, led by the Croatian Chamber of Economy, puts together policy instruments and key players for development of innovative technologies for the sustainable development of the Adriatic Sea (https://www.italy-croatia.eu/web/innovamare). The project aims at enhancing the cross-border cooperation among research, public and private stakeholders through creation of a Digital Innovation Hub (DIH). The goal is to increase effectiveness of innovation in underwater robotics and sensors to achieve and maintain a healthy and productive Adriatic Sea, as one of the crucial and strategic societal challenges existing at the cross-border level. Within InnovaMare, CNR ISMAR and INM institutes and OGS, in cooperation with the University of Zagreb and other project partners, contribute to developing a solution to access and monitor extremely shallow water by means of portable, modular, reconfigurable and highly maneuverable robotic vehicles. The identified vehicle is SWAMP, an innovative highly modular catamaran ASV recently developed by CNR-INM. SWAMP is characterised by small size, low draft, new materials, azimuth propulsion system for shallow waters and modular WiFi-based hardware&software architecture. Two SWAMP vehicles will be enhanced with a series of kits, tools and sensors to perform a series of strategic actions in the environmental monitoring of the Venice Lagoon: <br>i) An air-cushion-system-kit will be designed and developed. The vehicle will become a side-wall air-cushion-vehicle with reduction of drag and increase in speed. This will also increase the payload with a reduction of draft. <br>ii) An intelligent winch kit with a communication cable for the management of underwater sensors and tools.<br>iii) A GPS-RTK kit for highly accurate positioning in the range of centimeters.<br>iv) An Autonomous programmable device for image acquisition and processing based on the Guard1 camera. This camera acquires images content and, by means of a supervised machine learning approach, recognises/classifies features such as fish, zooplankton, seabed, infrastructures. The system is conceived for autonomous monitoring activities extended in time in fixed or mobile platforms.<br>v) A Multibeam Echo-sounder (MBES) coupled with an IMU (for pitch-roll compensation). MBES data can be used, also coupled with Cameras Imagery, through image-detection techniques for reconstruction and comprehensive knowledge of underwater environment and infrastructures. Possible analyses in coastal areas are: seabed mapping also for cultural heritage, offshore structures and resources and monitoring of biodiversity, hydrocarbon, marine litter, pollution.<br>vi) An underwater Radiometer for multiple analysis: temporal dynamics of optical properties of water; temporal dynamics of water turbidity from water reflectance; submerged vegetation and water depth mapping in optically shallow water; produce reference data for validation of satellite data.<br>vii) Automatic Nutrient Analyzer for real-time nutrient monitoring. This sensor measures nitrate with high accuracy over a wide range of environmental conditions (including extremely turbid and high CDOM conditions), from blue-ocean nitraclines to storm runoff in rivers and streams. <br>The final result of this pilot action is the creation of an innovative prototype platform for sea environmental monitoring. This will be validated through the analysis of results and draw up of guidelines for the improvement of underwater conditions.</p>

Dynamics of Excitatory Synaptic Components in Sustained Firing at Low Rates

2005 ◽  
Vol 93 (6) ◽  
pp. 3370-3380 ◽  
Author(s):  
Claire Wyart ◽  
Simona Cocco ◽  
Laurent Bourdieu ◽  
Jean-Francois Léger ◽  
Catherine Herr ◽  
...  
Keyword(s):  
Metabotropic Glutamate Receptor ◽  
Temporal Dynamics ◽  
Low Frequency ◽  
Firing Frequency ◽  
Excitatory Postsynaptic Potential ◽  
Metabotropic Glutamate ◽  
Wide Range ◽  
Nmdar Activation ◽  
Asynchronous Release ◽  
Low Rate

Sustained firing is necessary for the persistent activity associated with working memory. The relative contributions of the reverberation of excitation and of the temporal dynamics of the excitatory postsynaptic potential (EPSP) to the maintenance of activity are difficult to evaluate in classical preparations. We used simplified models of synchronous excitatory networks, hippocampal autapses and pairs, to study the synaptic mechanisms underlying firing at low rates. Calcium imaging and cell attached recordings showed that these neurons spontaneously fired bursts of action potentials that lasted for seconds over a wide range of frequencies. In 2-wk-old cells, the median firing frequency was low (11 ± 8.8 Hz), whereas in 3- to 4-wk-old cells, it decreased to a very low value (2 ± 1.3 Hz). In both cases, we have shown that the slowest synaptic component supported firing. In 2-wk-old autapses, antagonists of N-methyl-d-aspartate receptors (NMDARs) induced rare isolated spikes showing that the NMDA component of the EPSP was essential for bursts at low frequency. In 3- to 4-wk-old neurons, the very low frequency firing was maintained without the NMDAR activation. However EGTA-AM or α-methyl-4-carboxyphenylglycine (MCPG) removed the very slow depolarizing component of the EPSP and prevented the sustained firing at very low rate. A metabotropic glutamate receptor (mGluR)-activated calcium sensitive conductance is therefore responsible for a very slow synaptic component associated with firing at very low rate. In addition, our observations suggested that the asynchronous release of glutamate might participate also in the recurring bursting.

scholarly journals Ultra-fast accurate reconstruction of spiking activity from calcium imaging data

2018 ◽  
Vol 119 (5) ◽  
pp. 1863-1878 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Calcium Imaging ◽  
Reconstruction Method ◽  
Imaging Data ◽  
Decay Kinetics ◽  
Experimental Conditions ◽  
Model Free ◽  
Reconstruction Methods ◽  
Wide Range ◽  
Fast Processing ◽  
Spiking Activity

Calcium imaging provides an indirect observation of the underlying neural dynamics and enables the functional analysis of neuronal populations. However, the recorded fluorescence traces are temporally smeared, thus making the reconstruction of exact spiking activity challenging. Most of the established methods to tackle this issue are limited in dealing with issues such as the variability in the kinetics of fluorescence transients, fast processing of long-term data, high firing rates, and measurement noise. We propose a novel, heuristic reconstruction method to overcome these limitations. By using both synthetic and experimental data, we demonstrate the four main features of this method: 1) it accurately reconstructs both isolated spikes and within-burst spikes, and the spike count per fluorescence transient, from a given noisy fluorescence trace; 2) it performs the reconstruction of a trace extracted from 1,000,000 frames in less than 2 s; 3) it adapts to transients with different rise and decay kinetics or amplitudes, both within and across single neurons; and 4) it has only one key parameter, which we will show can be set in a nearly automatic way to an approximately optimal value. Furthermore, we demonstrate the ability of the method to effectively correct for fast and rather complex, slowly varying drifts as frequently observed in in vivo data. NEW & NOTEWORTHY Reconstruction of spiking activities from calcium imaging data remains challenging. Most of the established reconstruction methods not only have limitations in adapting to systematic variations in the data and fast processing of large amounts of data, but their results also depend on the user’s experience. To overcome these limitations, we present a novel, heuristic model-free-type method that enables an ultra-fast, accurate, near-automatic reconstruction from data recorded under a wide range of experimental conditions.

Multi-method approach combining isotopic tracers, anthropogenic contaminants and mapping to retrace socio-environmental trajectories of groundwater-dependent coastal hydrosystems

2020 ◽  
Author(s):  
Melanie Erostate ◽  
Frederic Huneau ◽  
Emilie Garel ◽  
Vanina Pasqualini
Keyword(s):  
Water Resources ◽  
Temporal Dynamics ◽  
Coastal Lagoons ◽  
Management Strategies ◽  
Current Status ◽  
Fresh Groundwater ◽  
Isotopic Tracers ◽  
Goods And Services ◽  
Wide Range ◽  
Multi Method Approach

<p>Coastal lagoons are unique and complex ecosystems. Resulting from both terrestrial (fresh groundwater and surface water) and marine water influences, these ecosystems are often maintained by direct or indirect groundwater supplies and collectively known as groundwater dependent ecosystems (GDEs). Because they provide a wide range of ecosystem goods and services on which a large part of the human population depends, coastal GDEs are considered as complex socio-economic and ecological component worldwide. The increasing human development in coastal areas induces yet a strong pressure on water resources and the expected effects of climate change could exacerbate the pressures on these environments. To limit the risks of degradation and to ensure the sustainability of ecosystem services, the implementation of proper water resources management strategies is essential. This requires a strong knowledge of the environmental and socio-economic trajectories of hydrosystems, and particularly of the behavior and role of groundwater.</p><p>To this end, only the combined use of several tools allows a global understanding of the spatial and temporal dynamics of the system. The correlation between isotopic tracers (<sup>18</sup>O, <sup>2</sup>H, <sup>3</sup>H, <sup>15</sup>N, <sup>11</sup>B), anthropogenic contaminants (organic micro pollutants) and mapping approaches (land-use and vulnerability) allows a historical analyze of the hydrosystem. In addition, to better constraint the hydrosystem hydrological behavior, it is also possible to highlight the current status of water resources, the historical legacy of pollutants and the consequences of past developments and practices, which continue to jeopardize the current quality of the water resource. This methodology was applied to a Mediterranean hydrosystem, in connection with a coastal lagoon (Corsica Island, France). The identification of degradation processes and their chronology could then be traced back in time.</p><p>It appears that the current deterioration is mainly due to a legacy pollution resulting from the development of policies implemented 60 years earlier. In the case of coastal GDEs that are highly anthropized and subject to ever-increasing development, this methodology proposes new key elements for the establishment of relevant management strategies to ensure the future sustainability of water resources.</p>

scholarly journals Robust identification of harmonic oscillator parameters using the adjoint Fokker–Planck equation

2017 ◽  
Vol 473 (2200) ◽  
pp. 20160894 ◽  
Author(s):  
E. Boujo ◽  
N. Noiray
Keyword(s):  
Temporal Dynamics ◽  
Oscillation Amplitude ◽  
Planck Equation ◽  
Van Der Pol Oscillator ◽  
Thermoacoustic Instabilities ◽  
Fokker Planck Equation ◽  
Forced Oscillators ◽  
Wide Range ◽  
Output Only ◽  
Fokker Planck

We present a model-based output-only method for identifying from time series the parameters governing the dynamics of stochastically forced oscillators. In this context, suitable models of the oscillator’s damping and stiffness properties are postulated, guided by physical understanding of the oscillatory phenomena. The temporal dynamics and the probability density function of the oscillation amplitude are described by a Langevin equation and its associated Fokker–Planck equation, respectively. One method consists in fitting the postulated analytical drift and diffusion coefficients with their estimated values, obtained from data processing by taking the short-time limit of the first two transition moments. However, this limit estimation loses robustness in some situations—for instance when the data are band-pass filtered to isolate the spectral contents of the oscillatory phenomena of interest. In this paper, we use a robust alternative where the adjoint Fokker–Planck equation is solved to compute Kramers–Moyal coefficients exactly, and an iterative optimization yields the parameters that best fit the observed statistics simultaneously in a wide range of amplitudes and time scales. The method is illustrated with a stochastic Van der Pol oscillator serving as a prototypical model of thermoacoustic instabilities in practical combustors, where system identification is highly relevant to control.

Calcium imaging in single neurons from brain slices using bioluminescent reporters

2009 ◽  
pp. NA-NA ◽  
Author(s):  
Estelle Drobac ◽  
Ludovic Tricoire ◽  
Alain-François Chaffotte ◽  
Elvire Guiot ◽  
Bertrand Lambolez
Keyword(s):  
Calcium Imaging ◽  
Brain Slices ◽  
Single Neurons ◽  
Bioluminescent Reporters

A review of recent advances in risk analysis for wildfire management

2013 ◽  
Vol 22 (1) ◽  
pp. 1 ◽  
Author(s):  
Carol Miller ◽  
Alan A. Ager
Keyword(s):  
Risk Analysis ◽  
Temporal Dynamics ◽  
Wildland Fire ◽  
Fire Risk ◽  
Future Research ◽  
Fuels Management ◽  
Recent Advances ◽  
Wide Range ◽  
Spatial Optimisation ◽  
Stochastic Events

Risk analysis evolved out of the need to make decisions concerning highly stochastic events, and is well suited to analyse the timing, location and potential effects of wildfires. Over the past 10 years, the application of risk analysis to wildland fire management has seen steady growth with new risk-based analytical tools that support a wide range of fire and fuels management planning scales from individual incidents to national, strategic interagency programs. After a brief review of the three components of fire risk – likelihood, intensity and effects – this paper reviews recent advances in quantifying and integrating these individual components of fire risk. We also review recent advances in addressing temporal dynamics of fire risk and spatial optimisation of fuels management activities. Risk analysis approaches have become increasingly quantitative and sophisticated but remain quite disparate. We suggest several necessary and fruitful directions for future research and development in wildfire risk analysis.

scholarly journals Individual-based multiple-unit dissimilarity: novel indices for assessing temporal variability in community composition

2021 ◽  
Author(s):  
Ryosuke Nakadai
Keyword(s):  
Community Composition ◽  
Beta Diversity ◽  
Temporal Dynamics ◽  
Diversity Indices ◽  
Barro Colorado Island ◽  
Forest Monitoring ◽  
List Type ◽  
Wide Range ◽  
Multiple Unit ◽  
Temporal Beta Diversity

AbstractBeta-diversity was originally defined spatially, i.e., as variation in community composition among sites in a region. However, the concept of beta-diversity has since been expanded to temporal contexts. This is referred to as “temporal beta-diversity”, and most approaches are simply an extension of spatial beta-diversity.The persistence and turnover of individuals over time is a unique feature of temporal beta-diversity. Nakadai (2020) introduced the “individual-based beta-diversity” concept, and provided novel indices to evaluate individual turnover and compositional shift by comparing individual turnover between two periods at a given site. However, the proposed individual-based indices are applicable only to pairwise dissimilarity, not to multiple-temporal (or more generally, multiple-unit) dissimilarity.Here, individual-based beta-diversity indices are extended to multiple-unit cases.To demonstrate the usage the properties of these indices compared to average pairwise measures, I applied them to a dataset for a permanent 50-ha forest dynamics plot on Barro Colorado Island in Panama.Information regarding “individuals” is generally missing from community ecology and biodiversity studies of temporal dynamics. In this context, the method proposed here is expected to be useful for addressing a wide range of research questions regarding temporal changes in biodiversity, especially studies using individual-tracked forest monitoring data.

Hybrid Integrate-and-Fire Model of a Bursting Neuron

2003 ◽  
Vol 15 (12) ◽  
pp. 2843-2862 ◽  
Author(s):  
Barbara J. Breen ◽  
William C. Gerken ◽  
Robert J. Butera
Keyword(s):  
Bifurcation Analysis ◽  
Original Model ◽  
Full Model ◽  
Single Neurons ◽  
Tonic Firing ◽  
Fire Model ◽  
Integrate And Fire ◽  
Bursting Neuron ◽  
Computational Speed ◽  
Wide Range

We present a reduction of a Hodgkin-Huxley (HH)—style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH-style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike events and mediate interactions between the subthreshold and spiking currents. The hybrid IF model successfully reproduces the dynamic behavior and temporal characteristics of the full model over a wide range of activity, including bursting and tonic firing. Comparisons of timed computer simulations of the reduced model and the original model for both single neurons and moderate lysized networks (n ≤ 500) show that this model offers improvement in computational speed over the HH-style bursting model.

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