temporal analysis
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2022 ◽  
Vol 178 ◽  
pp. 104402
Bokai Yang ◽  
Hengtao Tang ◽  
Ling Hao ◽  
John R. Rose

2022 ◽  
Vol 308 ◽  
pp. 118226
Christina Kockel ◽  
Lars Nolting ◽  
Jan Priesmann ◽  
Aaron Praktiknjo

2022 ◽  
Vol 15 ◽  
Sam R. J. Hoare ◽  
Paul H. Tewson ◽  
Shivani Sachdev ◽  
Mark Connor ◽  
Thomas E. Hughes ◽  

Neurons integrate inputs over different time and space scales. Fast excitatory synapses at boutons (ms and μm), and slow modulation over entire dendritic arbors (seconds and mm) are all ultimately combined to produce behavior. Understanding the timing of signaling events mediated by G-protein-coupled receptors is necessary to elucidate the mechanism of action of therapeutics targeting the nervous system. Measuring signaling kinetics in live cells has been transformed by the adoption of fluorescent biosensors and dyes that convert biological signals into optical signals that are conveniently recorded by microscopic imaging or by fluorescence plate readers. Quantifying the timing of signaling has now become routine with the application of equations in familiar curve fitting software to estimate the rates of signaling from the waveform. Here we describe examples of the application of these methods, including (1) Kinetic analysis of opioid signaling dynamics and partial agonism measured using cAMP and arrestin biosensors; (2) Quantifying the signaling activity of illicit synthetic cannabinoid receptor agonists measured using a fluorescent membrane potential dye; (3) Demonstration of multiplicity of arrestin functions from analysis of biosensor waveforms and quantification of the rates of these processes. These examples show how temporal analysis provides additional dimensions to enhance the understanding of GPCR signaling and therapeutic mechanisms in the nervous system.

2022 ◽  
Vol 53 (4) ◽  
pp. 447-456
Rajendra Prasad ◽  
Hari Singh

Aridity Anomaly Index (AAI), based on Thornthwaite’s water balance technique, has been used to identify the extent and persistence of aridity anomalies over 33 sub-divisions of India during a period of 10 years from 1990 to 1999. Regional and temporal analysis has been carried out to identify the areas and periods of intense and prolonged persistence.   This study has shown that 1992 was worst hit by the aridity conditions, which emerged in 5 or more fortnights. All sub-divisions of north India were affected by moderate aridity during 1990, 1992-94 and 1999. Similarly, all sub-divisions of peninsular India were influenced by moderate aridity during 1991, 1993 and 1999. Severe aridity appeared in all sub-divisions of peninsular India during 1990. The duration of severe aridity was less than that of moderate aridity during all years. Moderate and severe aridity appeared simultaneously in 5 or more fortnights in maximum 9 sub-divisions in 1992 and occurred during maximum 5 years in Madhya Maharashtra. Moderate aridity in 5 or more fortnights emerged each year during 1990 to 1999 in coastal Andhra Pradesh. In 1991, maximum 55% sub-divisions were affected by severe aridity in 9th  fortnight, whereas Saurashtra & Kutch was affected in 1996 and north Interior Karnataka in 1999 during maximum 7 fortnights.   In the year 1992, maximum number of sub-divisions under moderate and severe persistence was 70% and 24% respectively. In north India, moderate persistence appeared in east Rajasthan in all nine years except 1996, with its longest duration of 8 fortnights in 1995. West Madhya Pradesh, in peninsular India, was affected by moderate aridity during 7 fortnights in each year during the period of study from 1990 to 1999.

Agostino Torti ◽  
Marika Arena ◽  
Giovanni Azzone ◽  
Piercesare Secchi ◽  
Simone Vantini

AbstractThis paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10,000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.

Leila Sherafati ◽  
Hossein Aghamohammadi Zanjirabad ◽  
Saeed Behzadi

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.

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