Real-time characterization of the spatio-temporal dynamics of deformable mirrors

Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.

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A novel localization based biosensor, SEDAR, visualizes spatio-temporal dynamics of activated Ras in endogenous Drosophila tissues

10.1101/432997 ◽

2018 ◽

Author(s):

Rishita Changede

Keyword(s):

Plasma Membrane ◽

Growth Factor ◽

Real Time ◽

Tyrosine Kinases ◽

Temporal Dynamics ◽

Three Dimensional ◽

Growth Factor Receptor ◽

Leading Edge ◽

Border Cells ◽

Spatio Temporal

AbstractChemokine signaling via growth factor receptor tyrosine kinases (RTKs) regulates development, differentiation, growth and disease implying that it is involved in a myriad of cellular processes. A single RTK, for example the Epidermal Growth Factor Receptor (EGFR), is used repeatedly for a multitude of developmental programs. Quantitative differences in magnitude and duration of RTK signaling can bring about different signaling outcomes. Understanding this complex RTK signals requires real time visualization of the signal. To visualize spatio-temporal signaling dynamics, a biosensor called SEnsitive Detection of Activated Ras (SEDAR) was developed. It is a localization-based sensor that binds to activated Ras directly downstream of the endogenous activated RTKs. This binding was reversible and SEDAR expression did not cause any detectable perturbation of the endogenous signal. Using SEDAR, endogenous guidance signaling was visualized during RTK mediated chemotaxis of border cells in Drosophila ovary. SEDAR localized to both the leading and rear end of the cluster but polarized at the leading edge of the cluster. Perturbation of RTKs that led to delays in forward migration of the cluster correlated with loss of SEDAR polarization in the cluster. Gliding or tumbling behavior of border cells was a directly related to the high or low magnitude of SEDAR polarization respectively, in the leading cell showing that signal polarization at the plasma membrane provided information for the migratory behavior. Further, SEDAR localization to the plasma membrane detected EGFR mediated signaling in five distinct developmental contexts. Hence SEDAR, a novel biosensor could be used as a valuable tool to study the dynamics of endogenous Ras activation in real time downstream of RTKs, in three-dimensional tissues, at an unprecedented spatial and temporal resolution.

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Large scale spatio-temporal patterns of road development in the Amazon rainforest

Environmental Conservation ◽

10.1017/s0376892913000520 ◽

2013 ◽

Vol 41 (3) ◽

pp. 253-264 ◽

Cited By ~ 4

Author(s):

SADIA E. AHMED ◽

ROBERT M. EWERS ◽

MATTHEW J. SMITH

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Temporal Patterns ◽

Biodiversity Loss ◽

Road Density ◽

Road Development ◽

Spatio Temporal ◽

Dominant Direction ◽

Dynamics Of Populations

SUMMARYThere is burgeoning interest in predicting road development because of the wide ranging important socioeconomic and environmental issues that roads present, including the close links between road development, deforestation and biodiversity loss. This is especially the case in developing nations, which are high in natural resources, where road development is rapid and often not centrally managed. Characterization of large scale spatio-temporal patterns in road network development has been greatly overlooked to date. This paper examines the spatio-temporal dynamics of road density across the Brazilian Amazon and assesses the relative contributions of local versus neighbourhood effects for temporal changes in road density at regional scales. To achieve this, a combination of statistical analyses and model-data fusion techniques inspired by studies of spatio-temporal dynamics of populations in ecology and epidemiology were used. The emergent development may be approximated by local growth that is logistic through time and directional dispersal. The current rates and dominant direction of development may be inferred, by assuming that roads develop at a rate of 55 km per year. Large areas of the Amazon will be subject to extensive anthropogenic change should the observed patterns of road development continue.

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Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

10.1101/032656 ◽

2015 ◽

Cited By ~ 5

Author(s):

Radoslaw Cichy ◽

Dimitrios Pantazis ◽

Aude Oliva

Keyword(s):

Object Recognition ◽

Temporal Dynamics ◽

Imaging Techniques ◽

Visual Object ◽

Data Sets ◽

Visual Object Recognition ◽

Occipital Pole ◽

Spatio Temporal ◽

The Brain

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

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Characterization and spatio-temporal dynamics of Cylindrospermopsis raciborski in an Amazonian Lagoon, Ecuador.

Enfoque UTE ◽

10.29019/enfoqueute.v9n2.195 ◽

2018 ◽

Vol 9 (2) ◽

pp. 117-124

Author(s):

Judith Venegas ◽

Pablo Castillejo Pons ◽

Susana Chamorro ◽

Ivonne Carrillo ◽

Eduardo Lobo

Keyword(s):

Fresh Water ◽

Environmental Conditions ◽

Scientific Literature ◽

Temporal Dynamics ◽

Cylindrospermopsis Raciborskii ◽

Human Consumption ◽

Distribution Maps ◽

Spatio Temporal ◽

Recreational Use

The cyanobacteria Cylindrospermopsis raciborskii, is a fresh water ubiquitous species from tropical to temperate weather. It is potentially capable of producing toxins. Thus it is necessary to monitor its presence in fresh waters associated to recreational use activities and human consumption. There are official reports and one thesis reporting the presence of C. raciborskii in Ecuador. Nevertheless, this country does not appear in the latest distribution maps of this species in the scientific literature. In this article, we report the presence of C. raciborskii in Ecuador, together with the characterization of the environmental conditions of one of the habitats where this species is present: the Limoncocha lagoon, province of Sucumbíos.

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MEASURING SYNCHRONIZATION IN THE EPILEPTIC BRAIN: A COMPARISON OF DIFFERENT APPROACHES

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127407019330 ◽

2007 ◽

Vol 17 (10) ◽

pp. 3539-3544 ◽

Cited By ~ 31

Author(s):

HANNES OSTERHAGE ◽

FLORIAN MORMANN ◽

MATTHÄUS STANIEK ◽

KLAUS LEHNERTZ

Keyword(s):

Prospective Studies ◽

Temporal Dynamics ◽

Brain Regions ◽

Relative Merit ◽

Seizure Prediction ◽

Free Interval ◽

Linear And Nonlinear ◽

Spatio Temporal

We investigate the relative merit of different linear and nonlinear synchronization measures for a characterization of the spatio-temporal dynamics of the epileptic process. Analyzing long-lasting multichannel electroencephalographic recordings from more than 20 epilepsy patients we show that all measures are able to identify brain regions of pathological synchronization associated with epilepsy, even during the seizure-free interval, and are able to detect a long-lasting transitional preseizure state. These findings render synchronization measures attractive for future prospective studies on seizure prediction.

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Spatio-Temporal Mutational Profile Appearances of Swedish SARS-CoV-2 during the Early Pandemic

Viruses ◽

10.3390/v12091026 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1026 ◽

Cited By ~ 2

Author(s):

Jiaxin Ling ◽

Rachel A. Hickman ◽

Jinlin Li ◽

Xi Lu ◽

Johanna F. Lindahl ◽

...

Keyword(s):

Genetic Variants ◽

Temporal Dynamics ◽

Evolutionary Rate ◽

Viral Pathogenesis ◽

Bayesian Phylogenetic Analysis ◽

Community Transmission ◽

Spatio Temporal ◽

Insight Into ◽

Over Time

Background: During the COVID-19 pandemic, the virus evolved, and we therefore aimed to provide an insight into which genetic variants were enriched, and how they spread in Sweden. Methods: We analyzed 348 Swedish SARS-CoV-2 sequences freely available from GISAID obtained from 7 February 2020 until 14 May 2020. Results: We identified 14 variant sites ≥5% frequency in the population. Among those sites, the D936Y substitution in the viral Spike protein was under positive selection. The variant sites can distinguish 11 mutational profiles in Sweden. Nine of the profiles appeared in Stockholm in March 2020. Mutational profiles 3 (B.1.1) and 6 (B.1), which contain the D936Y mutation, became the predominant profiles over time, spreading from Stockholm to other Swedish regions during April and the beginning of May. Furthermore, Bayesian phylogenetic analysis indicated that SARS-CoV-2 could have emerged in Sweden on 27 December 2019, and community transmission started on February 1st with an evolutionary rate of 1.5425 × 10−3 substitutions per year. Conclusions: Our study provides novel knowledge on the spatio-temporal dynamics of Swedish SARS-CoV-2 variants during the early pandemic. Characterization of these viral variants can provide precious insights on viral pathogenesis and can be valuable for diagnostic and drug development approaches.

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TMOD-15. IDENTIFYING THE SPATIAL AND TEMPORAL DYNAMICS OF GLIOBLASTOMA SUBPOPULATIONS WITHIN INDIVIDUAL PATIENTS

Neuro-Oncology ◽

10.1093/neuonc/noz175.1114 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi265-vi266

Author(s):

Bethan Morris ◽

Lee Curtin ◽

Andrea Hawkins-Daarud ◽

Bernard Bendok ◽

Maciej Mrugala ◽

...

Keyword(s):

Treatment Response ◽

Temporal Dynamics ◽

Dynamic Information ◽

Treatment Regimes ◽

Spatially Resolved ◽

Spatial And Temporal Dynamics ◽

Genetic Features ◽

Spatio Temporal ◽

Insight Into

Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

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