A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images

Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

Gene regulation relies on transcription factors (TFs) exploring the nucleus searching their targets. So far, most studies have focused on how fast TFs diffuse, underestimating the role of nuclear architecture. We implemented a single-molecule tracking assay to determine TFs dynamics. We found that c-Myc is a global explorer of the nucleus. In contrast, the positive transcription elongation factor P-TEFb is a local explorer that oversamples its environment. Consequently, each c-Myc molecule is equally available for all nuclear sites while P-TEFb reaches its targets in a position-dependent manner. Our observations are consistent with a model in which the exploration geometry of TFs is restrained by their interactions with nuclear structures and not by exclusion. The geometry-controlled kinetics of TFs target-search illustrates the influence of nuclear architecture on gene regulation, and has strong implications on how proteins react in the nucleus and how their function can be regulated in space and time.Nikon France (Research contract)France. Agence nationale de la recherche (PCV DYNAFT 08-PCVI-0013)France. Agence nationale de la recherche (DynamIC ANR-12-BSV5-0018)Fondation pour la recherche médicaleNetherlands Organization for Scientific Research (Rubicon fellowship

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

Gene regulation relies on transcription factors (TFs) exploring the nucleus searching their targets. So far, most studies have focused on how fast TFs diffuse, underestimating the role of nuclear architecture. We implemented a single-molecule tracking assay to determine TFs dynamics. We found that c-Myc is a global explorer of the nucleus. In contrast, the positive transcription elongation factor P-TEFb is a local explorer that oversamples its environment. Consequently, each c-Myc molecule is equally available for all nuclear sites while P-TEFb reaches its targets in a position-dependent manner. Our observations are consistent with a model in which the exploration geometry of TFs is restrained by their interactions with nuclear structures and not by exclusion. The geometry-controlled kinetics of TFs target-search illustrates the influence of nuclear architecture on gene regulation, and has strong implications on how proteins react in the nucleus and how their function can be regulated in space and time

Noise-induced stabilization of collective dynamics

ACKNOWLEDGMENT This work has been financially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 642563 (COSMOS).Peer reviewedPublisher PD

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu

Cardiac organoid technology and computational processing of cardiac physiology for advanced drug screening applications

Stem cell technology has gained considerable recognition since its inception to advance disease modeling and drug screening. This is especially true for tissues that are difficult to study due to tissue sensitivity and limited regenerative capacity, such as the heart. Previous work in stem cell-derived cardiac tissue has exploited how we can engineer biologically functional heart tissue by providing the appropriate external stimuli to facilitate tissue development. The goal of this dissertation is to explore the potentials of stem cell cardiac organoid models to recapitulate heart development and implement analytical computational tools to study cardiac physiology. These new tools were implemented as potential advancements in drug screening applications for better predictions of drug-related cardiotoxicity. Cardiac organoids, generated via micropatterning techniques, were explored to determine how controlling engineering parameters, specifically the geometry, direct tissue fate and organoid function. The advantage of cardiac organoid models is the ability to recapitulate and study human tissue morphogenesis and development, which has currently been restricted through animal models. The cardiac organoids demonstrated responsiveness manifested as impairments to tissue formation and contractile functions as a result of developmental drug toxicity. Single-cell genomic characterization of cardiac organoids unveiled a co-emergence of cardiac and endoderm tissue, which is seen in vivo through paracrine signaling between the liver and heart. We then implemented computational tools based on nonlinear mathematical analysis to evaluate the cardiac physiological drug response of stem cell-derived cardiomyocytes. This dissertation discusses in vitro tissue platforms as well as computational tools to study drug-induced cardiotoxicity. Using these tools, we can extend current toolboxes of understanding cardiac physiology for advanced investigations of stem-cell based cardiac tissue engineering