scholarly journals A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning

2013 ◽  
pp. 63-72 ◽  
Author(s):  
Ole-Christoffer Granmo ◽  
Jaziar Radianti ◽  
Morten Goodwin ◽  
Julie Dugdale ◽  
Parvaneh Sarshar ◽  
...  
Keyword(s):  
Probabilistic Model ◽  
Evacuation Planning ◽  
Crowd Dynamics ◽  
Spatio Temporal

A spatio-temporal probabilistic model of hazard- and crowd dynamics for evacuation planning in disasters

2014 ◽  
Vol 42 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Jaziar Radianti ◽  
Ole-Christoffer Granmo ◽  
Parvaneh Sarshar ◽  
Morten Goodwin ◽  
Julie Dugdale ◽  
...  
Keyword(s):  
Probabilistic Model ◽  
Evacuation Planning ◽  
Crowd Dynamics ◽  
Spatio Temporal

Supercharging Crowd Dynamics Estimation in Disasters via Spatio-Temporal Deep Neural Network

2017 ◽  
Author(s):  
Fang-Zhou Jiang ◽  
Lei Zhong ◽  
Kanchana Thilakarathna ◽  
Aruna Seneviratne ◽  
Kiyoshi Takano ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Crowd Dynamics ◽  
Spatio Temporal

Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτ Samples Through a Relaxed Probabilistic Model

2018 ◽  
pp. 37-49 ◽  
Author(s):  
Patryk Filipiak ◽  
Rutger Fick ◽  
Alexandra Petiet ◽  
Mathieu Santin ◽  
Anne-Charlotte Philippe ◽  
...  
Keyword(s):  
Probabilistic Model ◽  
Spatio Temporal

Personalized Spatio-Temporal OLAP Queries Suggestion Based on User Behavior and a New Similarity Measure

2019 ◽  
pp. 105-128
Author(s):  
Olfa Layouni ◽  
Jalel Akaichi
Keyword(s):  
Probabilistic Model ◽  
User Behavior ◽  
Search Behavior ◽  
Relevant Information ◽  
Current User ◽  
Data Cube ◽  
Temporal Data ◽  
Query Suggestion ◽  
Spatio Temporal ◽  
User Search Behavior

Spatio-temporal data warehouses store enormous amount of data. They are usually exploited by spatio-temporal OLAP systems to extract relevant information. For extracting interesting information, the current user launches spatio-temporal OLAP (ST-OLAP) queries to navigate within a geographic data cube (Geo-cube). Very often choosing which part of the Geo-cube to navigate further, and thus designing the forthcoming ST-OLAP query, is a difficult task. So, to help the current user refine his queries after launching in the geo-cube his current query, we need a ST-OLAP queries suggestion by exploiting a Geo-cube. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, first, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. Then, they propose a personalized query suggestion model based on users' search behavior, where they inject relevance between queries in the current session and current user' search behavior into a basic probabilistic model.

scholarly journals A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

2019 ◽  
Author(s):  
Olivera Stojanović ◽  
Johannes Leugering ◽  
Gordon Pipa ◽  
Stéphane Ghozzi ◽  
Alexander Ullrich
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Infectious Diseases ◽  
Probabilistic Model ◽  
Domain Knowledge ◽  
Training Data ◽  
List Type ◽  
Model Predictions ◽  
Bayesian Monte Carlo ◽  
Spatio Temporal

AbstractIn this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-arthhh4model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.Author summaryWhy was this study done?Statistical modeling is invaluable to public-health policy as it helps understand and anticipate the dynamics of the spread of infectious diseases. The available training data is often limited and reported with a low spatial and temporal resolution. This poses a challenge and makes it particularly important to incorporate domain knowledge and prior assumptions to guide the modeling process.In order to evaluate the trustworthiness and reliability of a model’s predictions, it is crucial to be able to interpret the model and quantify the model uncertainty.To address this, we develop an interpretable model that uses Bayesian inference (rather than commonly used maximum likelihood estimation) and provides a probability distribution over inferred parameters.What did the researchers do and find?We develop and test a single probabilistic model that learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviral enteritis and Lyme borreliosis) at the county level one week ahead of time.We employ a Bayesian Monte Carlo regression approach that provides an estimate of the full probability distribution over inferred parameters as well as model predictions.The model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.The predictive performance of our model compares favorably with a contemporary reference model for all diseases tested.What do these findings mean?Interpretable predictive models can be applied to surveillance data to gain insights into the dynamics of infectious diseases.Probabilistic modeling approaches provide a suitable framework for many challenges of working with epidemiological data.

E3 ubiquitin ligases

2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson
Keyword(s):  
Protein Complexes ◽  
Loss Of Function ◽  
Direct Role ◽  
Cellular Processes ◽  
Substrate Protein ◽  
Protein Ubiquitination ◽  
C Terminus ◽  
Full Complement ◽  
Spatio Temporal ◽  
Eukaryotic Organisms

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.

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