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Pubmed Article
Large scale identification and categorization of protein sequences using structured logistic regression.
PUBLISHED: 01-01-2014
Structured Logistic Regression (SLR) is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well-suited for this task. The classification of P-type ATPases, a large family of ATP-driven membrane pumps transporting essential cations, was selected as a test-case that would generate important biological information as well as provide a proof-of-concept for the application of SLR to a large scale bioinformatics problem.
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 (DHL) (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.
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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
Authors: Noah S. Philip, S. Louisa Carpenter, Lawrence H. Sweet.
Institutions: Alpert Medical School, Brown University, University of Georgia.
Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD). Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions. Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.
Medicine, Issue 89, default mode network, neuroimaging, functional magnetic resonance imaging, diffusion tensor imaging, structural connectivity, functional connectivity, posttraumatic stress disorder
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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation
Authors: Yufen Goh, Melissa J. Fullwood, Huay Mei Poh, Su Qin Peh, Chin Thing Ong, Jingyao Zhang, Xiaoan Ruan, Yijun Ruan.
Institutions: Agency for Science, Technology and Research, Singapore, A*STAR-Duke-NUS Neuroscience Research Partnership, Singapore, National University of Singapore, Singapore.
Genomes are organized into three-dimensional structures, adopting higher-order conformations inside the micron-sized nuclear spaces 7, 2, 12. Such architectures are not random and involve interactions between gene promoters and regulatory elements 13. The binding of transcription factors to specific regulatory sequences brings about a network of transcription regulation and coordination 1, 14. Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) was developed to identify these higher-order chromatin structures 5,6. Cells are fixed and interacting loci are captured by covalent DNA-protein cross-links. To minimize non-specific noise and reduce complexity, as well as to increase the specificity of the chromatin interaction analysis, chromatin immunoprecipitation (ChIP) is used against specific protein factors to enrich chromatin fragments of interest before proximity ligation. Ligation involving half-linkers subsequently forms covalent links between pairs of DNA fragments tethered together within individual chromatin complexes. The flanking MmeI restriction enzyme sites in the half-linkers allow extraction of paired end tag-linker-tag constructs (PETs) upon MmeI digestion. As the half-linkers are biotinylated, these PET constructs are purified using streptavidin-magnetic beads. The purified PETs are ligated with next-generation sequencing adaptors and a catalog of interacting fragments is generated via next-generation sequencers such as the Illumina Genome Analyzer. Mapping and bioinformatics analysis is then performed to identify ChIP-enriched binding sites and ChIP-enriched chromatin interactions 8. We have produced a video to demonstrate critical aspects of the ChIA-PET protocol, especially the preparation of ChIP as the quality of ChIP plays a major role in the outcome of a ChIA-PET library. As the protocols are very long, only the critical steps are shown in the video.
Genetics, Issue 62, ChIP, ChIA-PET, Chromatin Interactions, Genomics, Next-Generation Sequencing
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A Restriction Enzyme Based Cloning Method to Assess the In vitro Replication Capacity of HIV-1 Subtype C Gag-MJ4 Chimeric Viruses
Authors: Daniel T. Claiborne, Jessica L. Prince, Eric Hunter.
Institutions: Emory University, Emory University.
The protective effect of many HLA class I alleles on HIV-1 pathogenesis and disease progression is, in part, attributed to their ability to target conserved portions of the HIV-1 genome that escape with difficulty. Sequence changes attributed to cellular immune pressure arise across the genome during infection, and if found within conserved regions of the genome such as Gag, can affect the ability of the virus to replicate in vitro. Transmission of HLA-linked polymorphisms in Gag to HLA-mismatched recipients has been associated with reduced set point viral loads. We hypothesized this may be due to a reduced replication capacity of the virus. Here we present a novel method for assessing the in vitro replication of HIV-1 as influenced by the gag gene isolated from acute time points from subtype C infected Zambians. This method uses restriction enzyme based cloning to insert the gag gene into a common subtype C HIV-1 proviral backbone, MJ4. This makes it more appropriate to the study of subtype C sequences than previous recombination based methods that have assessed the in vitro replication of chronically derived gag-pro sequences. Nevertheless, the protocol could be readily modified for studies of viruses from other subtypes. Moreover, this protocol details a robust and reproducible method for assessing the replication capacity of the Gag-MJ4 chimeric viruses on a CEM-based T cell line. This method was utilized for the study of Gag-MJ4 chimeric viruses derived from 149 subtype C acutely infected Zambians, and has allowed for the identification of residues in Gag that affect replication. More importantly, the implementation of this technique has facilitated a deeper understanding of how viral replication defines parameters of early HIV-1 pathogenesis such as set point viral load and longitudinal CD4+ T cell decline.
Infectious Diseases, Issue 90, HIV-1, Gag, viral replication, replication capacity, viral fitness, MJ4, CEM, GXR25
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Quantitative Analyses of all Influenza Type A Viral Hemagglutinins and Neuraminidases using Universal Antibodies in Simple Slot Blot Assays
Authors: Caroline Gravel, Changgui Li, Junzhi Wang, Anwar M Hashem, Bozena Jaentschke, Gary Van Domselaar, Runtao He, Xuguang Li.
Institutions: Health canada, The State Food and Drug Administration, Beijing, University of Ottawa, King Abdulaziz University, Public Health Agency of Canada.
Hemagglutinin (HA) and neuraminidase (NA) are two surface proteins of influenza viruses which are known to play important roles in the viral life cycle and the induction of protective immune responses1,2. As the main target for neutralizing antibodies, HA is currently used as the influenza vaccine potency marker and is measured by single radial immunodiffusion (SRID)3. However, the dependence of SRID on the availability of the corresponding subtype-specific antisera causes a minimum of 2-3 months delay for the release of every new vaccine. Moreover, despite evidence that NA also induces protective immunity4, the amount of NA in influenza vaccines is not yet standardized due to a lack of appropriate reagents or analytical method5. Thus, simple alternative methods capable of quantifying HA and NA antigens are desirable for rapid release and better quality control of influenza vaccines. Universally conserved regions in all available influenza A HA and NA sequences were identified by bioinformatics analyses6-7. One sequence (designated as Uni-1) was identified in the only universally conserved epitope of HA, the fusion peptide6, while two conserved sequences were identified in neuraminidases, one close to the enzymatic active site (designated as HCA-2) and the other close to the N-terminus (designated as HCA-3)7. Peptides with these amino acid sequences were synthesized and used to immunize rabbits for the production of antibodies. The antibody against the Uni-1 epitope of HA was able to bind to 13 subtypes of influenza A HA (H1-H13) while the antibodies against the HCA-2 and HCA-3 regions of NA were capable of binding all 9 NA subtypes. All antibodies showed remarkable specificity against the viral sequences as evidenced by the observation that no cross-reactivity to allantoic proteins was detected. These universal antibodies were then used to develop slot blot assays to quantify HA and NA in influenza A vaccines without the need for specific antisera7,8. Vaccine samples were applied onto a PVDF membrane using a slot blot apparatus along with reference standards diluted to various concentrations. For the detection of HA, samples and standard were first diluted in Tris-buffered saline (TBS) containing 4M urea while for the measurement of NA they were diluted in TBS containing 0.01% Zwittergent as these conditions significantly improved the detection sensitivity. Following the detection of the HA and NA antigens by immunoblotting with their respective universal antibodies, signal intensities were quantified by densitometry. Amounts of HA and NA in the vaccines were then calculated using a standard curve established with the signal intensities of the various concentrations of the references used. Given that these antibodies bind to universal epitopes in HA or NA, interested investigators could use them as research tools in immunoassays other than the slot blot only.
Immunology, Issue 50, Virology, influenza, hemagglutinin, neuraminidase, quantification, universal antibody
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
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A Quantitative Fitness Analysis Workflow
Authors: A.P. Banks, C. Lawless, D.A. Lydall.
Institutions: Newcastle University Medical School.
Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA. The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking. QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1. Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow. We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.
Physiology, Issue 66, Medicine, Robotic, microbial, culture, yeast, array, library, high-throughput, analysis, fitness, growth rate, quantitative, solid agar
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Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy (f3D-SIM)
Authors: Lynne Turnbull, Michael P. Strauss, Andrew T. F. Liew, Leigh G. Monahan, Cynthia B. Whitchurch, Elizabeth J. Harry.
Institutions: University of Technology, Sydney.
Imaging of biological samples using fluorescence microscopy has advanced substantially with new technologies to overcome the resolution barrier of the diffraction of light allowing super-resolution of live samples. There are currently three main types of super-resolution techniques – stimulated emission depletion (STED), single-molecule localization microscopy (including techniques such as PALM, STORM, and GDSIM), and structured illumination microscopy (SIM). While STED and single-molecule localization techniques show the largest increases in resolution, they have been slower to offer increased speeds of image acquisition. Three-dimensional SIM (3D-SIM) is a wide-field fluorescence microscopy technique that offers a number of advantages over both single-molecule localization and STED. Resolution is improved, with typical lateral and axial resolutions of 110 and 280 nm, respectively and depth of sampling of up to 30 µm from the coverslip, allowing for imaging of whole cells. Recent advancements (fast 3D-SIM) in the technology increasing the capture rate of raw images allows for fast capture of biological processes occurring in seconds, while significantly reducing photo-toxicity and photobleaching. Here we describe the use of one such method to image bacterial cells harboring the fluorescently-labelled cytokinetic FtsZ protein to show how cells are analyzed and the type of unique information that this technique can provide.
Molecular Biology, Issue 91, super-resolution microscopy, fluorescence microscopy, OMX, 3D-SIM, Blaze, cell division, bacteria, Bacillus subtilis, Staphylococcus aureus, FtsZ, Z ring constriction
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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
Authors: Ashwin Prakash, Jason Bechtel, Alexei Fedorov.
Institutions: University of Toledo Health Science Campus.
Non-coding genomic regions in complex eukaryotes, including intergenic areas, introns, and untranslated segments of exons, are profoundly non-random in their nucleotide composition and consist of a complex mosaic of sequence patterns. These patterns include so-called Mid-Range Inhomogeneity (MRI) regions -- sequences 30-10000 nucleotides in length that are enriched by a particular base or combination of bases (e.g. (G+T)-rich, purine-rich, etc.). MRI regions are associated with unusual (non-B-form) DNA structures that are often involved in regulation of gene expression, recombination, and other genetic processes (Fedorova & Fedorov 2010). The existence of a strong fixation bias within MRI regions against mutations that tend to reduce their sequence inhomogeneity additionally supports the functionality and importance of these genomic sequences (Prakash et al. 2009). Here we demonstrate a freely available Internet resource -- the Genomic MRI program package -- designed for computational analysis of genomic sequences in order to find and characterize various MRI patterns within them (Bechtel et al. 2008). This package also allows generation of randomized sequences with various properties and level of correspondence to the natural input DNA sequences. The main goal of this resource is to facilitate examination of vast regions of non-coding DNA that are still scarcely investigated and await thorough exploration and recognition.
Genetics, Issue 51, bioinformatics, computational biology, genomics, non-randomness, signals, gene regulation, DNA conformation
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Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology
Authors: Andreas Florian Haas, Ben Knowles, Yan Wei Lim, Tracey McDole Somera, Linda Wegley Kelly, Mark Hatay, Forest Rohwer.
Institutions: San Diego State University, University of California San Diego.
Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.
Environmental Sciences, Issue 93, dissolved organic carbon, particulate organic matter, nutrients, DAPI, SYBR, microbial metagenomics, viral metagenomics, marine environment
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A Practical Guide to Phylogenetics for Nonexperts
Authors: Damien O'Halloran.
Institutions: The George Washington University.
Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this topic and so it presents inherent problems. Here we compile a practical introduction to phylogenetics for nonexperts. We outline in a step-by-step manner, a pipeline for generating reliable phylogenies from gene sequence datasets. We begin with a user-guide for similarity search tools via online interfaces as well as local executables. Next, we explore programs for generating multiple sequence alignments followed by protocols for using software to determine best-fit models of evolution. We then outline protocols for reconstructing phylogenetic relationships via maximum likelihood and Bayesian criteria and finally describe tools for visualizing phylogenetic trees. While this is not by any means an exhaustive description of phylogenetic approaches, it does provide the reader with practical starting information on key software applications commonly utilized by phylogeneticists. The vision for this article would be that it could serve as a practical training tool for researchers embarking on phylogenetic studies and also serve as an educational resource that could be incorporated into a classroom or teaching-lab.
Basic Protocol, Issue 84, phylogenetics, multiple sequence alignments, phylogenetic tree, BLAST executables, basic local alignment search tool, Bayesian models
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Measuring Cation Transport by Na,K- and H,K-ATPase in Xenopus Oocytes by Atomic Absorption Spectrophotometry: An Alternative to Radioisotope Assays
Authors: Katharina L. Dürr, Neslihan N. Tavraz, Susan Spiller, Thomas Friedrich.
Institutions: Technical University of Berlin, Oregon Health & Science University.
Whereas cation transport by the electrogenic membrane transporter Na+,K+-ATPase can be measured by electrophysiology, the electroneutrally operating gastric H+,K+-ATPase is more difficult to investigate. Many transport assays utilize radioisotopes to achieve a sufficient signal-to-noise ratio, however, the necessary security measures impose severe restrictions regarding human exposure or assay design. Furthermore, ion transport across cell membranes is critically influenced by the membrane potential, which is not straightforwardly controlled in cell culture or in proteoliposome preparations. Here, we make use of the outstanding sensitivity of atomic absorption spectrophotometry (AAS) towards trace amounts of chemical elements to measure Rb+ or Li+ transport by Na+,K+- or gastric H+,K+-ATPase in single cells. Using Xenopus oocytes as expression system, we determine the amount of Rb+ (Li+) transported into the cells by measuring samples of single-oocyte homogenates in an AAS device equipped with a transversely heated graphite atomizer (THGA) furnace, which is loaded from an autosampler. Since the background of unspecific Rb+ uptake into control oocytes or during application of ATPase-specific inhibitors is very small, it is possible to implement complex kinetic assay schemes involving a large number of experimental conditions simultaneously, or to compare the transport capacity and kinetics of site-specifically mutated transporters with high precision. Furthermore, since cation uptake is determined on single cells, the flux experiments can be carried out in combination with two-electrode voltage-clamping (TEVC) to achieve accurate control of the membrane potential and current. This allowed e.g. to quantitatively determine the 3Na+/2K+ transport stoichiometry of the Na+,K+-ATPase and enabled for the first time to investigate the voltage dependence of cation transport by the electroneutrally operating gastric H+,K+-ATPase. In principle, the assay is not limited to K+-transporting membrane proteins, but it may work equally well to address the activity of heavy or transition metal transporters, or uptake of chemical elements by endocytotic processes.
Biochemistry, Issue 72, Chemistry, Biophysics, Bioengineering, Physiology, Molecular Biology, electrochemical processes, physical chemistry, spectrophotometry (application), spectroscopic chemical analysis (application), life sciences, temperature effects (biological, animal and plant), Life Sciences (General), Na+,K+-ATPase, H+,K+-ATPase, Cation Uptake, P-type ATPases, Atomic Absorption Spectrophotometry (AAS), Two-Electrode Voltage-Clamp, Xenopus Oocytes, Rb+ Flux, Transversely Heated Graphite Atomizer (THGA) Furnace, electrophysiology, animal model
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Authors: Karin Hauffen, Eugene Bart, Mark Brady, Daniel Kersten, Jay Hegdé.
Institutions: Georgia Health Sciences University, Georgia Health Sciences University, Georgia Health Sciences University, Palo Alto Research Center, Palo Alto Research Center, University of Minnesota .
In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2. Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper. We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have. Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.
Neuroscience, Issue 69, machine learning, brain, classification, category learning, cross-modal perception, 3-D prototyping, inference
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Authors: James Smadbeck, Meghan B. Peterson, George A. Khoury, Martin S. Taylor, Christodoulos A. Floudas.
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 (, 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
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
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
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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
Authors: C. R. Gallistel, Fuat Balci, David Freestone, Aaron Kheifets, Adam King.
Institutions: Rutgers University, Koç University, New York University, Fairfield University.
We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.
Behavior, Issue 84, genetics, cognitive mechanisms, behavioral screening, learning, memory, timing
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Authors: Johannes Felix Buyel, Rainer Fischer.
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
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
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
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Authors: Wenan Chen, Ashwin Belle, Charles Cockrell, Kevin R. Ward, Kayvan Najarian.
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
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Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth
Authors: Sabrina Lin, Shawn Fonteno, Shruthi Satish, Bir Bhanu, Prue Talbot.
Institutions: University of California, University of California, University of California, University of California.
Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software. Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT. To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images. To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time. The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.
Cellular Biology, Issue 39, hESC, matrigel, stem cells, video bioinformatics, colony, growth
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Authors: Viktor Martyanov, Robert H. Gross.
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
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Using Learning Outcome Measures to assess Doctoral Nursing Education
Authors: Glenn H. Raup, Jeff King, Romana J. Hughes, Natasha Faidley.
Institutions: Harris College of Nursing and Health Sciences, Texas Christian University.
Education programs at all levels must be able to demonstrate successful program outcomes. Grades alone do not represent a comprehensive measurement methodology for assessing student learning outcomes at either the course or program level. The development and application of assessment rubrics provides an unequivocal measurement methodology to ensure a quality learning experience by providing a foundation for improvement based on qualitative and quantitatively measurable, aggregate course and program outcomes. Learning outcomes are the embodiment of the total learning experience and should incorporate assessment of both qualitative and quantitative program outcomes. The assessment of qualitative measures represents a challenge for educators in any level of a learning program. Nursing provides a unique challenge and opportunity as it is the application of science through the art of caring. Quantification of desired student learning outcomes may be enhanced through the development of assessment rubrics designed to measure quantitative and qualitative aspects of the nursing education and learning process. They provide a mechanism for uniform assessment by nursing faculty of concepts and constructs that are otherwise difficult to describe and measure. A protocol is presented and applied to a doctoral nursing education program with recommendations for application and transformation of the assessment rubric to other education programs. Through application of these specially designed rubrics, all aspects of an education program can be adequately assessed to provide information for program assessment that facilitates the closure of the gap between desired and actual student learning outcomes for any desired educational competency.
Medicine, Issue 40, learning, outcomes, measurement, program, assessment, rubric
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Interview: Protein Folding and Studies of Neurodegenerative Diseases
Authors: Susan Lindquist.
Institutions: MIT - Massachusetts Institute of Technology.
In this interview, Dr. Lindquist describes relationships between protein folding, prion diseases and neurodegenerative disorders. The problem of the protein folding is at the core of the modern biology. In addition to their traditional biochemical functions, proteins can mediate transfer of biological information and therefore can be considered a genetic material. This recently discovered function of proteins has important implications for studies of human disorders. Dr. Lindquist also describes current experimental approaches to investigate the mechanism of neurodegenerative diseases based on genetic studies in model organisms.
Neuroscience, issue 17, protein folding, brain, neuron, prion, neurodegenerative disease, yeast, screen, Translational Research
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In Vitro Nuclear Assembly Using Fractionated Xenopus Egg Extracts
Authors: Marie Cross, Maureen Powers.
Institutions: Emory University.
Nuclear membrane assembly is an essential step in the cell division cycle; this process can be replicated in the test tube by combining Xenopus sperm chromatin, cytosol, and light membrane fractions. Complete nuclei are formed, including nuclear membranes with pore complexes, and these reconstituted nuclei are capable of normal nuclear processes.
Cellular Biology, Issue 19, Current Protocols Wiley, Xenopus Egg Extracts, Nuclear Assembly, Nuclear Membrane
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.