It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4.
Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.
The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5 involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6 and density volume rendering7. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.
19 Related JoVE Articles!
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Institutions: Princeton University.
The aim of de novo
protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo
protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (https://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
A Protocol for Computer-Based Protein Structure and Function Prediction
Institutions: University of Michigan , University of Kansas.
Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.
Biochemistry, Issue 57, On-line server, I-TASSER, protein structure prediction, function prediction
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g.
, signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.
The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria
Institutions: Auburn University , Keesler Air Force Base.
A structurally transformed lytic bacteriophage having a broad host range of Staphylococcus aureus
strains and a penicillin-binding protein (PBP 2a) antibody conjugated latex beads have been utilized to create a biosensor designed for discrimination of methicillin resistant (MRSA) and sensitive (MSSA) S. aureus
. The lytic phages have been converted into phage spheroids by contact with water-chloroform interface. Phage spheroid monolayers have been moved onto a biosensor surface by Langmuir-Blodgett (LB) technique 3
. The created biosensors have been examined by a quartz crystal microbalance with dissipation tracking (QCM-D) to evaluate bacteria-phage interactions. Bacteria-spheroid interactions led to reduced resonance frequency and a rise in dissipation energy for both MRSA and MSSA strains. After the bacterial binding, these sensors have been further exposed to the penicillin-binding protein antibody latex beads. Sensors analyzed with MRSA responded to PBP 2a antibody beads; although sensors inspected with MSSA gave no response. This experimental distinction determines an unambiguous discrimination between methicillin resistant and sensitive S. aureus
strains. Equally bound and unbound bacteriophages suppress bacterial growth on surfaces and in water suspensions. Once lytic phages are changed into spheroids, they retain their strong lytic activity and show high bacterial capture capability. The phage and phage spheroids can be utilized for testing and sterilization of antibiotic resistant microorganisms. Other applications may include use in bacteriophage therapy and antimicrobial surfaces.
Bioengineering, Issue 75, Microbiology, Infectious Diseases, Infection, Medicine, Immunology, Cellular Biology, Molecular Biology, Genetics, Anatomy, Physiology, Bacteria, Pharmacology, Staphylococcus, Bacteriophages, phage, Binding, Competitive, Biophysics, surface properties (nonmetallic materials), surface wave acoustic devices (electronic design), sensors, Lytic phage spheroids, QCM-D, Langmuir-Blodgett (LB) monolayers, MRSA, Staphylococcus aureus, assay
Designing Silk-silk Protein Alloy Materials for Biomedical Applications
Institutions: Rowan University, Rowan University, Cooper Medical School of Rowan University, Rowan University.
Fibrous proteins display different sequences and structures that have been used for various applications in biomedical fields such as biosensors, nanomedicine, tissue regeneration, and drug delivery. Designing materials based on the molecular-scale interactions between these proteins will help generate new multifunctional protein alloy biomaterials with tunable properties. Such alloy material systems also provide advantages in comparison to traditional synthetic polymers due to the materials biodegradability, biocompatibility, and tenability in the body. This article used the protein blends of wild tussah silk (Antheraea pernyi
) and domestic mulberry silk (Bombyx mori
) as an example to provide useful protocols regarding these topics, including how to predict protein-protein interactions by computational methods, how to produce protein alloy solutions, how to verify alloy systems by thermal analysis, and how to fabricate variable alloy materials including optical materials with diffraction gratings, electric materials with circuits coatings, and pharmaceutical materials for drug release and delivery. These methods can provide important information for designing the next generation multifunctional biomaterials based on different protein alloys.
Bioengineering, Issue 90, protein alloys, biomaterials, biomedical, silk blends, computational simulation, implantable electronic devices
Identification of Post-translational Modifications of Plant Protein Complexes
Institutions: University of Warwick, Norwich Research Park, The Australian National University.
Plants adapt quickly to changing environments due to elaborate perception and signaling systems. During pathogen attack, plants rapidly respond to infection via
the recruitment and activation of immune complexes. Activation of immune complexes is associated with post-translational modifications (PTMs) of proteins, such as phosphorylation, glycosylation, or ubiquitination. Understanding how these PTMs are choreographed will lead to a better understanding of how resistance is achieved.
Here we describe a protein purification method for nucleotide-binding leucine-rich repeat (NB-LRR)-interacting proteins and the subsequent identification of their post-translational modifications (PTMs). With small modifications, the protocol can be applied for the purification of other plant protein complexes. The method is based on the expression of an epitope-tagged version of the protein of interest, which is subsequently partially purified by immunoprecipitation and subjected to mass spectrometry for identification of interacting proteins and PTMs.
This protocol demonstrates that: i). Dynamic changes in PTMs such as phosphorylation can be detected by mass spectrometry; ii). It is important to have sufficient quantities of the protein of interest, and this can compensate for the lack of purity of the immunoprecipitate; iii). In order to detect PTMs of a protein of interest, this protein has to be immunoprecipitated to get a sufficient quantity of protein.
Plant Biology, Issue 84, plant-microbe interactions, protein complex purification, mass spectrometry, protein phosphorylation, Prf, Pto, AvrPto, AvrPtoB
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Institutions: RWTH Aachen University, Fraunhofer Gesellschaft.
Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.
Bioengineering, Issue 83, design of experiments (DoE), transient protein expression, plant-derived biopharmaceuticals, promoter, 5'UTR, fluorescent reporter protein, model building, incubation conditions, monoclonal antibody
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Institutions: Dartmouth College, University of Rhode Island, Dartmouth College.
Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design1,2
. Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols1,3-5
. Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.
Biochemistry, Issue 85, Immunoassay, Protein Immunogenicity, MHC II, T cell epitope, High Throughput Screen, Deimmunization, Vaccine Design
Analysis of Oxidative Stress in Zebrafish Embryos
Institutions: University of Torino, Vesalius Research Center, VIB.
High levels of reactive oxygen species (ROS) may cause a change of cellular redox state towards oxidative stress condition. This situation causes oxidation of molecules (lipid, DNA, protein) and leads to cell death. Oxidative stress also impacts the progression of several pathological conditions such as diabetes, retinopathies, neurodegeneration, and cancer. Thus, it is important to define tools to investigate oxidative stress conditions not only at the level of single cells but also in the context of whole organisms. Here, we consider the zebrafish embryo as a useful in vivo
system to perform such studies and present a protocol to measure in vivo
oxidative stress. Taking advantage of fluorescent ROS probes and zebrafish transgenic fluorescent lines, we develop two different methods to measure oxidative stress in vivo
: i) a “whole embryo ROS-detection method” for qualitative measurement of oxidative stress and ii) a “single-cell ROS detection method” for quantitative measurements of oxidative stress. Herein, we demonstrate the efficacy of these procedures by increasing oxidative stress in tissues by oxidant agents and physiological or genetic methods. This protocol is amenable for forward genetic screens and it will help address cause-effect relationships of ROS in animal models of oxidative stress-related pathologies such as neurological disorders and cancer.
Developmental Biology, Issue 89, Danio rerio, zebrafish embryos, endothelial cells, redox state analysis, oxidative stress detection, in vivo ROS measurements, FACS (fluorescence activated cell sorter), molecular probes
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.
Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via
quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
RNA Secondary Structure Prediction Using High-throughput SHAPE
Institutions: Frederick National Laboratory for Cancer Research.
Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico
RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.
Genetics, Issue 75, Molecular Biology, Biochemistry, Virology, Cancer Biology, Medicine, Genomics, Nucleic Acid Probes, RNA Probes, RNA, High-throughput SHAPE, Capillary electrophoresis, RNA structure, RNA probing, RNA folding, secondary structure, DNA, nucleic acids, electropherogram, synthesis, transcription, high throughput, sequencing
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Institutions: University of Zurich.
Mori's Uncanny Valley Hypothesis1,2
proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness
) (Figure 1
). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6
. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7
. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.
Behavior, Issue 76, Neuroscience, Neurobiology, Molecular Biology, Psychology, Neuropsychology, uncanny valley, functional magnetic resonance imaging, fMRI, categorical perception, virtual reality, avatar, human likeness, Mori, uncanny valley hypothesis, perception, magnetic resonance imaging, MRI, imaging, clinical techniques
Analytical Techniques for Assaying Nitric Oxide Bioactivity
Institutions: University of Texas Health Science Center at Houston , Baylor College of Medicine .
Nitric oxide (NO) is a diatomic free radical that is extremely short lived in biological systems (less than 1 second in circulating blood)1
. NO may be considered one of the most important signaling molecules produced in our body, regulating essential functions including but not limited to regulation of blood pressure, immune response and neural communication. Therefore its accurate detection and quantification in biological matrices is critical to understanding the role of NO in health and disease. With such a short physiological half life of NO, alternative strategies for the detection of reaction products of NO biochemistry have been developed. The quantification of relevant NO metabolites in multiple biological compartments provides valuable information with regards to in vivo
NO production, bioavailability and metabolism. Simply sampling a single compartment such as blood or plasma may not always provide an accurate assessment of whole body NO status, particularly in tissues. The ability to compare blood with select tissues in experimental animals will help bridge the gap between basic science and clinical medicine as far as diagnostic and prognostic utility of NO biomarkers in health and disease. Therefore, extrapolation of plasma or blood NO status to specific tissues of interest is no longer a valid approach. As a result, methods continue to be developed and validated which allow the detection and quantification of NO and NO-related products/metabolites in multiple compartments of experimental animals in vivo
. The established paradigm of NO biochemistry from production by NO synthases to activation of soluble guanylyl cyclase (sGC) to eventual oxidation to nitrite (NO2-
) and nitrate (NO3-
) may only represent part of NO's effects in vivo
. The interaction of NO and NO-derived metabolites with protein thiols, secondary amines, and metals to form S-nitrosothiols (RSNOs), N-nitrosamines (RNNOs), and nitrosyl-heme respectively represent cGMP-independent effects of NO and are likely just as important physiologically as activation of sGC by NO. A true understanding of NO in physiology is derived from in vivo
experiments sampling multiple compartments simultaneously. Nitric oxide (NO) methodology is a complex and often confusing science and the focus of many debates and discussion concerning NO biochemistry. The elucidation of new mechanisms and signaling pathways involving NO hinges on our ability to specifically, selectively and sensitively detect and quantify NO and all relevant NO products and metabolites in complex biological matrices. Here, we present a method for the rapid and sensitive analysis of nitrite and nitrate by HPLC as well as detection of free NO in biological samples using in vitro
ozone based chemiluminescence with chemical derivitazation to determine molecular source of NO as well as ex vivo
with organ bath myography.
Medicine, Issue 64, Molecular Biology, Nitric oxide, nitrite, nitrate, endothelium derived relaxing factor, HPLC, chemiluminscence
Bioengineering Human Microvascular Networks in Immunodeficient Mice
Institutions: Harvard Medical School.
The future of tissue engineering and cell-based therapies for tissue regeneration will likely rely on our ability to generate functional vascular networks in vivo. In this regard, the search for experimental models to build blood vessel networks in vivo is of utmost importance 1
. The feasibility of bioengineering microvascular networks in vivo was first shown using human tissue-derived mature endothelial cells (ECs) 2-4
; however, such autologous endothelial cells present problems for wide clinical use, because they are difficult to obtain in sufficient quantities and require harvesting from existing vasculature. These limitations have instigated the search for other sources of ECs. The identification of endothelial colony-forming cells (ECFCs) in blood presented an opportunity to non-invasively obtain ECs 5-7
. We and other authors have shown that adult and cord blood-derived ECFCs have the capacity to form functional vascular networks in vivo 7-11
. Importantly, these studies have also shown that to obtain stable and durable vascular networks, ECFCs require co-implantation with perivascular cells. The assay we describe here illustrates this concept: we show how human cord blood-derived ECFCs can be combined with bone marrow-derived mesenchymal stem cells (MSCs) as a single cell suspension in a collagen/fibronectin/fibrinogen gel to form a functional human vascular network within 7 days after implantation into an immunodeficient mouse. The presence of human ECFC-lined lumens containing host erythrocytes can be seen throughout the implants indicating not only the formation (de novo
) of a vascular network, but also the development of functional anastomoses with the host circulatory system. This murine model of bioengineered human vascular network is ideally suited for studies on the cellular and molecular mechanisms of human vascular network formation and for the development of strategies to vascularize engineered tissues.
Bioengineering, Issue 53, vascular network, blood vessel, vasculogenesis, angiogenesis, endothelial progenitor cells, endothelial colony-forming cells, mesenchymal stem cells, collagen gel, fibrin gel, tissue engineering, regenerative medicine
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Institutions: Virginia Commonwealth University, Virginia Commonwealth University Reanimation Engineering Science (VCURES) Center, Virginia Commonwealth University, Virginia Commonwealth University, Virginia Commonwealth University.
In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.
Medicine, Issue 74, Biomedical Engineering, Molecular Biology, Neurobiology, Biophysics, Physiology, Anatomy, Brain CT Image Processing, CT, Midline Shift, Intracranial Pressure Pre-screening, Gaussian Mixture Model, Shape Matching, Machine Learning, traumatic brain injury, TBI, imaging, clinical techniques
Cross-Modal Multivariate Pattern Analysis
Institutions: University of Southern California.
Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4
. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5
or, analogously, the content of speech from activity in early auditory cortices6
Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog?
In two previous studies7,8
, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10
, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices.
Neuroscience, Issue 57, perception, sensory, cross-modal, top-down, mental imagery, fMRI, MRI, neuroimaging, multivariate pattern analysis, MVPA
Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Institutions: Dartmouth College.
SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference1
. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data1
. In this article, we utilize a web version of SCOPE2
to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs3,4
and has been used in other studies5-8
The three algorithms that comprise SCOPE are BEAM9
, which finds non-degenerate motifs (ACCGGT), PRISM10
, which finds degenerate motifs (ASCGWT), and SPACER11
, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well.
Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor.
Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run.
Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail1,2,9-11
Genetics, Issue 51, gene regulation, computational biology, algorithm, promoter sequence motif
Principles of Site-Specific Recombinase (SSR) Technology
Institutions: Max Plank Institute for Molecular Cell Biology and Genetics, Dresden.
Site-specific recombinase (SSR) technology allows the manipulation of gene structure to explore gene function and has become an integral tool of molecular biology. Site-specific recombinases are proteins that bind to distinct DNA target sequences. The Cre/lox system was first described in bacteriophages during the 1980's. Cre recombinase is a Type I topoisomerase that catalyzes site-specific recombination of DNA between two loxP (locus of X-over P1) sites. The Cre/lox system does not require any cofactors. LoxP sequences contain distinct binding sites for Cre recombinases that surround a directional core sequence where recombination and rearrangement takes place. When cells contain loxP sites and express the Cre recombinase, a recombination event occurs. Double-stranded DNA is cut at both loxP sites by the Cre recombinase, rearranged, and ligated ("scissors and glue"). Products of the recombination event depend on the relative orientation of the asymmetric sequences.
SSR technology is frequently used as a tool to explore gene function. Here the gene of interest is flanked with Cre target sites loxP ("floxed"). Animals are then crossed with animals expressing the Cre recombinase under the control of a tissue-specific promoter. In tissues that express the Cre recombinase it binds to target sequences and excises the floxed gene. Controlled gene deletion allows the investigation of gene function in specific tissues and at distinct time points. Analysis of gene function employing SSR technology --- conditional mutagenesis -- has significant advantages over traditional knock-outs where gene deletion is frequently lethal.
Cellular Biology, Issue 15, Molecular Biology, Site-Specific Recombinase, Cre recombinase, Cre/lox system, transgenic animals, transgenic technology
Silicon Microchips for Manipulating Cell-cell Interaction
Institutions: MIT - Massachusetts Institute of Technology.
The role of the cellular microenvironment is recognized as crucial in determining cell fate and function in virtually all mammalian tissues from development to malignant transformation. In particular, interaction with neighboring stroma has been implicated in a plethora of biological phenomena; however, conventional techniques limit the ability to interrogate the spatial and dynamic elements of such interactions.
In Micromechanical Reconfigurable Culture (RC), we employ a micromachined silicon substrate with moving parts to dynamically control cell-cell interactions through mechanical repositioning. Previously, this method has been applied to investigate intercellular communication in co-cultures of hepatocytes and non-parenchymal cells, demonstrating time-dependent interactions and a limited range for soluble signaling 1
Here, we describe in detail the preparation and use of the RC system. We begin by demonstrating the handling of the device parts using tweezers, including actuating between the gap and contact configurations (cell populations separated by a narrow 80-µm gap, or in direct intimate contact). Next, we detail the process of preparing the substrates for culture, and the multi-step cell seeding process required for obtaining confluent cell monolayers. Using live microscopy, we then illustrate real-time manipulation of cells between the different possible experimental configurations. Finally, we demonstrate the steps required in order to regenerate the device surface for reuse: toluene and piranha cleaning, polystyrene coating, and oxygen plasma treatment.
Issue 7, tissue engineering, MEMS, microfabrication, microenvironment, Bioengineering