The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.
24 Related JoVE Articles!
High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry
Institutions: Medical College of Wisconsin, Stanford University School of Medicine, Medical College of Wisconsin, Hong Kong University, Johns Hopkins University School of Medicine, Medical College of Wisconsin.
There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the heart, and improving strategies to accurately model heart disease. The potential of exploiting human induced pluripotent stem cell (hiPSC) technology to generate cardiac muscle “in a dish” for these applications continues to generate high enthusiasm. In recent years, the ability to efficiently generate cardiomyogenic cells from human pluripotent stem cells (hPSCs) has greatly improved, offering us new opportunities to model very early stages of human cardiac development not otherwise accessible. In contrast to many previous methods, the cardiomyocyte differentiation protocol described here does not require cell aggregation or the addition of Activin A or BMP4 and robustly generates cultures of cells that are highly positive for cardiac troponin I and T (TNNI3, TNNT2), iroquois-class homeodomain protein IRX-4 (IRX4), myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC2v) and myosin regulatory light chain 2, atrial isoform (MLC2a) by day 10 across all human embryonic stem cell (hESC) and hiPSC lines tested to date. Cells can be passaged and maintained for more than 90 days in culture. The strategy is technically simple to implement and cost-effective. Characterization of cardiomyocytes derived from pluripotent cells often includes the analysis of reference markers, both at the mRNA and protein level. For protein analysis, flow cytometry is a powerful analytical tool for assessing quality of cells in culture and determining subpopulation homogeneity. However, technical variation in sample preparation can significantly affect quality of flow cytometry data. Thus, standardization of staining protocols should facilitate comparisons among various differentiation strategies. Accordingly, optimized staining protocols for the analysis of IRX4, MLC2v, MLC2a, TNNI3, and TNNT2 by flow cytometry are described.
Cellular Biology, Issue 91, human induced pluripotent stem cell, flow cytometry, directed differentiation, cardiomyocyte, IRX4, TNNI3, TNNT2, MCL2v, MLC2a
Test Samples for Optimizing STORM Super-Resolution Microscopy
Institutions: National Physical Laboratory.
STORM is a recently developed super-resolution microscopy technique with up to 10 times better resolution than standard fluorescence microscopy techniques. However, as the image is acquired in a very different way than normal, by building up an image molecule-by-molecule, there are some significant challenges for users in trying to optimize their image acquisition. In order to aid this process and gain more insight into how STORM works we present the preparation of 3 test samples and the methodology of acquiring and processing STORM super-resolution images with typical resolutions of between 30-50 nm. By combining the test samples with the use of the freely available rainSTORM processing software it is possible to obtain a great deal of information about image quality and resolution. Using these metrics it is then possible to optimize the imaging procedure from the optics, to sample preparation, dye choice, buffer conditions, and image acquisition settings. We also show examples of some common problems that result in poor image quality, such as lateral drift, where the sample moves during image acquisition and density related problems resulting in the 'mislocalization' phenomenon.
Molecular Biology, Issue 79, Genetics, Bioengineering, Biomedical Engineering, Biophysics, Basic Protocols, HeLa Cells, Actin Cytoskeleton, Coated Vesicles, Receptor, Epidermal Growth Factor, Actins, Fluorescence, Endocytosis, Microscopy, STORM, super-resolution microscopy, nanoscopy, cell biology, fluorescence microscopy, test samples, resolution, actin filaments, fiducial markers, epidermal growth factor, cell, imaging
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Institutions: University of Maine.
Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.
Basic Protocol, Issue 82, Microscopy, Super-resolution imaging, Multicolor, single molecule, FPALM, Localization microscopy, fluorescent proteins
Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
Institutions: University of Northern Colorado, Arizona State University, Iowa State University.
The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee prosthesis, and 2) contrast the effects of the proposed technique and that of using intact limb inertial properties on joint kinetic estimates during walking in unilateral, transtibial amputees. An oscillation and reaction board system was validated and shown to be reliable when measuring inertial properties of known geometrical solids. When direct measurements of inertial properties of the prosthesis were used in inverse dynamics modeling of the lower extremity compared with inertial estimates based on an intact shank and foot, joint kinetics at the hip and knee were significantly lower during the swing phase of walking. Differences in joint kinetics during stance, however, were smaller than those observed during swing. Therefore, researchers focusing on the swing phase of walking should consider the impact of prosthesis inertia property estimates on study outcomes. For stance, either one of the two inertial models investigated in our study would likely lead to similar outcomes with an inverse dynamics assessment.
Bioengineering, Issue 87, prosthesis inertia, amputee locomotion, below-knee prosthesis, transtibial amputee
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
Laboratory Estimation of Net Trophic Transfer Efficiencies of PCB Congeners to Lake Trout (Salvelinus namaycush) from Its Prey
Institutions: U. S. Geological Survey, Grand Valley State University, Shedd Aquarium.
A technique for laboratory estimation of net trophic transfer efficiency (γ) of polychlorinated biphenyl (PCB) congeners to piscivorous fish from their prey is described herein. During a 135-day laboratory experiment, we fed bloater (Coregonus hoyi
) that had been caught in Lake Michigan to lake trout (Salvelinus namaycush
) kept in eight laboratory tanks. Bloater is a natural prey for lake trout. In four of the tanks, a relatively high flow rate was used to ensure relatively high activity by the lake trout, whereas a low flow rate was used in the other four tanks, allowing for low lake trout activity. On a tank-by-tank basis, the amount of food eaten by the lake trout on each day of the experiment was recorded. Each lake trout was weighed at the start and end of the experiment. Four to nine lake trout from each of the eight tanks were sacrificed at the start of the experiment, and all 10 lake trout remaining in each of the tanks were euthanized at the end of the experiment. We determined concentrations of 75 PCB congeners in the lake trout at the start of the experiment, in the lake trout at the end of the experiment, and in bloaters fed to the lake trout during the experiment. Based on these measurements, γ was calculated for each of 75 PCB congeners in each of the eight tanks. Mean γ was calculated for each of the 75 PCB congeners for both active and inactive lake trout. Because the experiment was replicated in eight tanks, the standard error about mean γ could be estimated. Results from this type of experiment are useful in risk assessment models to predict future risk to humans and wildlife eating contaminated fish under various scenarios of environmental contamination.
Environmental Sciences, Issue 90, trophic transfer efficiency, polychlorinated biphenyl congeners, lake trout, activity, contaminants, accumulation, risk assessment, toxic equivalents
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
Institutions: Case Western Reserve University.
Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as plant-soil feedbacks (PSF) have large effects on plant performance, and create environmental heterogeneity that depends on the community composition. Understanding the importance of PSF for plant community assembly necessitates understanding of the role of heterogeneity in PSF, in addition to mean PSF effects. Here, we describe a protocol for manipulating plant-induced soil heterogeneity. Two example experiments are presented: (1) a field experiment with a 6-patch grid of soils to measure plant population responses and (2) a greenhouse experiment with 2-patch soils to measure individual plant responses. Soils can be collected from the zone of root influence (soils from the rhizosphere and directly adjacent to the rhizosphere) of plants in the field from conspecific and heterospecific plant species. Replicate collections are used to avoid pseudoreplicating soil samples. These soils are then placed into separate patches for heterogeneous treatments or mixed for a homogenized treatment. Care should be taken to ensure that heterogeneous and homogenized treatments experience the same degree of soil disturbance. Plants can then be placed in these soil treatments to determine the effect of plant-induced soil heterogeneity on plant performance. We demonstrate that plant-induced heterogeneity results in different outcomes than predicted by traditional coexistence models, perhaps because of the dynamic nature of these feedbacks. Theory that incorporates environmental heterogeneity influenced by the assembling community and additional empirical work is needed to determine when heterogeneity intrinsic to the assembling community will result in different assembly outcomes compared with heterogeneity extrinsic to the community composition.
Environmental Sciences, Issue 85, Coexistence, community assembly, environmental drivers, plant-soil feedback, soil heterogeneity, soil microbial communities, soil patch
Flat Mount Preparation for Observation and Analysis of Zebrafish Embryo Specimens Stained by Whole Mount In situ Hybridization
Institutions: University of Notre Dame.
The zebrafish embryo is now commonly used for basic and biomedical research to investigate the genetic control of developmental processes and to model congenital abnormalities. During the first day of life, the zebrafish embryo progresses through many developmental stages including fertilization, cleavage, gastrulation, segmentation, and the organogenesis of structures such as the kidney, heart, and central nervous system. The anatomy of a young zebrafish embryo presents several challenges for the visualization and analysis of the tissues involved in many of these events because the embryo develops in association with a round yolk mass. Thus, for accurate analysis and imaging of experimental phenotypes in fixed embryonic specimens between the tailbud and 20 somite stage (10 and 19 hours post fertilization (hpf), respectively), such as those stained using whole mount in situ
hybridization (WISH), it is often desirable to remove the embryo from the yolk ball and to position it flat on a glass slide. However, performing a flat mount procedure can be tedious. Therefore, successful and efficient flat mount preparation is greatly facilitated through the visual demonstration of the dissection technique, and also helped by using reagents that assist in optimal tissue handling. Here, we provide our WISH protocol for one or two-color detection of gene expression in the zebrafish embryo, and demonstrate how the flat mounting procedure can be performed on this example of a stained fixed specimen. This flat mounting protocol is broadly applicable to the study of many embryonic structures that emerge during early zebrafish development, and can be implemented in conjunction with other staining methods performed on fixed embryo samples.
Developmental Biology, Issue 89, animals, vertebrates, fishes, zebrafish, growth and development, morphogenesis, embryonic and fetal development, organogenesis, natural science disciplines, embryo, whole mount in situ hybridization, flat mount, deyolking, imaging
Laboratory-determined Phosphorus Flux from Lake Sediments as a Measure of Internal Phosphorus Loading
Institutions: Grand Valley State University.
Eutrophication is a water quality issue in lakes worldwide, and there is a critical need to identify and control nutrient sources. Internal phosphorus (P) loading from lake sediments can account for a substantial portion of the total P load in eutrophic, and some mesotrophic, lakes. Laboratory determination of P release rates from sediment cores is one approach for determining the role of internal P loading and guiding management decisions. Two principal alternatives to experimental determination of sediment P release exist for estimating internal load: in situ
measurements of changes in hypolimnetic P over time and P mass balance. The experimental approach using laboratory-based sediment incubations to quantify internal P load is a direct method, making it a valuable tool for lake management and restoration.
Laboratory incubations of sediment cores can help determine the relative importance of internal vs. external P loads, as well as be used to answer a variety of lake management and research questions. We illustrate the use of sediment core incubations to assess the effectiveness of an aluminum sulfate (alum) treatment for reducing sediment P release. Other research questions that can be investigated using this approach include the effects of sediment resuspension and bioturbation on P release.
The approach also has limitations. Assumptions must be made with respect to: extrapolating results from sediment cores to the entire lake; deciding over what time periods to measure nutrient release; and addressing possible core tube artifacts. A comprehensive dissolved oxygen monitoring strategy to assess temporal and spatial redox status in the lake provides greater confidence in annual P loads estimated from sediment core incubations.
Environmental Sciences, Issue 85, Limnology, internal loading, eutrophication, nutrient flux, sediment coring, phosphorus, lakes
Analysis of Nephron Composition and Function in the Adult Zebrafish Kidney
Institutions: University of Notre Dame.
The zebrafish model has emerged as a relevant system to study kidney development, regeneration and disease. Both the embryonic and adult zebrafish kidneys are composed of functional units known as nephrons, which are highly conserved with other vertebrates, including mammals. Research in zebrafish has recently demonstrated that two distinctive phenomena transpire after adult nephrons incur damage: first, there is robust regeneration within existing nephrons that replaces the destroyed tubule epithelial cells; second, entirely new nephrons are produced from renal progenitors in a process known as neonephrogenesis. In contrast, humans and other mammals seem to have only a limited ability for nephron epithelial regeneration. To date, the mechanisms responsible for these kidney regeneration phenomena remain poorly understood. Since adult zebrafish kidneys undergo both nephron epithelial regeneration and neonephrogenesis, they provide an outstanding experimental paradigm to study these events. Further, there is a wide range of genetic and pharmacological tools available in the zebrafish model that can be used to delineate the cellular and molecular mechanisms that regulate renal regeneration. One essential aspect of such research is the evaluation of nephron structure and function. This protocol describes a set of labeling techniques that can be used to gauge renal composition and test nephron functionality in the adult zebrafish kidney. Thus, these methods are widely applicable to the future phenotypic characterization of adult zebrafish kidney injury paradigms, which include but are not limited to, nephrotoxicant exposure regimes or genetic methods of targeted cell death such as the nitroreductase mediated cell ablation technique. Further, these methods could be used to study genetic perturbations in adult kidney formation and could also be applied to assess renal status during chronic disease modeling.
Cellular Biology, Issue 90,
zebrafish; kidney; nephron; nephrology; renal; regeneration; proximal tubule; distal tubule; segment; mesonephros; physiology; acute kidney injury (AKI)
Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Institutions: Yale University, Yale University, Yale University, Yale University, Massachusetts General Hospital, University of California, Irvine.
We describe experimental and statistical steps for creating dopamine movies of the brain from dynamic PET data. The movies represent minute-to-minute fluctuations of dopamine induced by smoking a cigarette. The smoker is imaged during a natural smoking experience while other possible confounding effects (such as head motion, expectation, novelty, or aversion to smoking repeatedly) are minimized.
We present the details of our unique analysis. Conventional methods for PET analysis estimate time-invariant kinetic model parameters which cannot capture short-term fluctuations in neurotransmitter release. Our analysis - yielding a dopamine movie - is based on our work with kinetic models and other decomposition techniques that allow for time-varying parameters 1-7
. This aspect of the analysis - temporal-variation - is key to our work. Because our model is also linear in parameters, it is practical, computationally, to apply at the voxel level. The analysis technique is comprised of five main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique 'HYPR' spatial filter 8
that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on 11
C-raclopride uptake. The statistical step compares the fit of our (lp-ntPET) model 7
to a conventional model 9
. Masking restricts treatment to those voxels best described by the new model. Visualization maps the dopamine function at each voxel to a color scale and produces a dopamine movie. Interim results and sample dopamine movies of cigarette smoking are presented.
Behavior, Issue 78, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Medicine, Anatomy, Physiology, Image Processing, Computer-Assisted, Receptors, Dopamine, Dopamine, Functional Neuroimaging, Binding, Competitive, mathematical modeling (systems analysis), Neurotransmission, transient, dopamine release, PET, modeling, linear, time-invariant, smoking, F-test, ventral-striatum, clinical techniques
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
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Institutions: Kean University, University of Wisconsin-Madison.
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.
Environmental Sciences, Issue 72, Computer Science, Behavior, Infectious Diseases, Geography, Cartography, Data Display, Disease Outbreaks, cartography, human behavior, Trajectory data, space-time activity, GPS, GIS, ArcGIS, spatiotemporal analysis, visualization, segmentation, density surface, density volume, exploratory data analysis, modelling
A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
Institutions: Emory University, Emory University, Emory University.
Experimental manipulations of sensory feedback during complex behavior have provided valuable insights into the computations underlying motor control and sensorimotor plasticity1
. Consistent sensory perturbations result in compensatory changes in motor output, reflecting changes in feedforward motor control that reduce the experienced feedback error. By quantifying how different sensory feedback errors affect human behavior, prior studies have explored how visual signals are used to recalibrate arm movements2,3
and auditory feedback is used to modify speech production4-7
. The strength of this approach rests on the ability to mimic naturalistic errors in behavior, allowing the experimenter to observe how experienced errors in production are used to recalibrate motor output.
Songbirds provide an excellent animal model for investigating the neural basis of sensorimotor control and plasticity8,9
. The songbird brain provides a well-defined circuit in which the areas necessary for song learning are spatially separated from those required for song production, and neural recording and lesion studies have made significant advances in understanding how different brain areas contribute to vocal behavior9-12
. However, the lack of a naturalistic error-correction paradigm - in which a known acoustic parameter is perturbed by the experimenter and then corrected by the songbird - has made it difficult to understand the computations underlying vocal learning or how different elements of the neural circuit contribute to the correction of vocal errors13
The technique described here gives the experimenter precise control over auditory feedback errors in singing birds, allowing the introduction of arbitrary sensory errors that can be used to drive vocal learning. Online sound-processing equipment is used to introduce a known perturbation to the acoustics of song, and a miniaturized headphones apparatus is used to replace a songbird's natural auditory feedback with the perturbed signal in real time. We have used this paradigm to perturb the fundamental frequency (pitch) of auditory feedback in adult songbirds, providing the first demonstration that adult birds maintain vocal performance using error correction14
. The present protocol can be used to implement a wide range of sensory feedback perturbations (including but not limited to pitch shifts) to investigate the computational and neurophysiological basis of vocal learning.
Neuroscience, Issue 69, Anatomy, Physiology, Zoology, Behavior, Songbird, psychophysics, auditory feedback, biology, sensorimotor learning
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
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Institutions: Stony Brook University, Cold Spring Harbor Laboratory, University of Texas at Dallas.
ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1
. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2
. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5
to more rigorous statistical models, e.g.
Hidden Markov Models (HMMs)6-8
. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9
, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11
and epigenetic data12
to illustrate its usefulness.
Genetics, Issue 70, Bioinformatics, Genomics, Molecular Biology, Cellular Biology, Immunology, Chromatin immunoprecipitation, ChIP-Seq, histone modifications, segmentation, Bayesian, Hidden Markov Models, epigenetics
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Institutions: University of Washington.
Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e
., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g
., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori
, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e
., perform group-level analysis) based on a common cortical coordinate space.
Neuroscience, Issue 68, Magnetoencephalography, MEG, Electroencephalography, EEG, audition, attention, inverse imaging
A Noninvasive Hair Sampling Technique to Obtain High Quality DNA from Elusive Small Mammals
Institutions: University of British Columbia, Okanagan Campus.
Noninvasive genetic sampling approaches are becoming increasingly important to study wildlife populations. A number of studies have reported using noninvasive sampling techniques to investigate population genetics and demography of wild populations1
. This approach has proven to be especially useful when dealing with rare or elusive species2
. While a number of these methods have been developed to sample hair, feces and other biological material from carnivores and medium-sized mammals, they have largely remained untested in elusive small mammals. In this video, we present a novel, inexpensive and noninvasive hair snare targeted at an elusive small mammal, the American pika (Ochotona princeps
). We describe the general set-up of the hair snare, which consists of strips of packing tape arranged in a web-like fashion and placed along travelling routes in the pikas’ habitat. We illustrate the efficiency of the snare at collecting a large quantity of hair that can then be collected and brought back to the lab. We then demonstrate the use of the DNA IQ system (Promega) to isolate DNA and showcase the utility of this method to amplify commonly used molecular markers including nuclear microsatellites, amplified fragment length polymorphisms (AFLPs), mitochondrial sequences (800bp) as well as a molecular sexing marker. Overall, we demonstrate the utility of this novel noninvasive hair snare as a sampling technique for wildlife population biologists. We anticipate that this approach will be applicable to a variety of small mammals, opening up areas of investigation within natural populations, while minimizing impact to study organisms.
Genetics, Issue 49, Conservation genetics, noninvasive genetic sampling, Hair snares, Microsatellites, AFLPs, American pika, Ochotona princeps
Rapid Diagnosis of Avian Influenza Virus in Wild Birds: Use of a Portable rRT-PCR and Freeze-dried Reagents in the Field
Institutions: USGS Western Ecological Research Center, University of California, Davis, University of California, Davis, University of Minnesota , Science Applications International Corporation.
Wild birds have been implicated in the spread of highly pathogenic avian influenza (HPAI) of the H5N1 subtype, prompting surveillance along migratory flyways. Sampling of wild birds for avian influenza virus (AIV) is often conducted in remote regions, but results are often delayed because of the need to transport samples to a laboratory equipped for molecular testing. Real-time reverse transcriptase polymerase chain reaction (rRT-PCR) is a molecular technique that offers one of the most accurate and sensitive methods for diagnosis of AIV. The previously strict lab protocols needed for rRT-PCR are now being adapted for the field. Development of freeze-dried (lyophilized) reagents that do not require cold chain, with sensitivity at the level of wet reagents has brought on-site remote testing to a practical goal.
Here we present a method for the rapid diagnosis of AIV in wild birds using an rRT-PCR unit (Ruggedized Advanced Pathogen Identification Device or RAPID, Idaho Technologies, Salt Lake City, UT) that employs lyophilized reagents (Influenza A Target 1 Taqman; ASAY-ASY-0109, Idaho Technologies). The reagents contain all of the necessary components for testing at appropriate concentrations in a single tube: primers, probes, enzymes, buffers and internal positive controls, eliminating errors associated with improper storage or handling of wet reagents. The portable unit performs a screen for Influenza A by targeting the matrix gene and yields results in 2-3 hours. Genetic subtyping is also possible with H5 and H7 primer sets that target the hemagglutinin gene.
The system is suitable for use on cloacal and oropharyngeal samples collected from wild birds, as demonstrated here on the migratory shorebird species, the western sandpiper (Calidrus mauri
) captured in Northern California. Animal handling followed protocols approved by the Animal Care and Use Committee of the U.S. Geological Survey Western Ecological Research Center and permits of the U.S. Geological Survey Bird Banding Laboratory. The primary advantage of this technique is to expedite diagnosis of wild birds, increasing the chances of containing an outbreak in a remote location. On-site diagnosis would also prove useful for identifying and studying infected individuals in wild populations. The opportunity to collect information on host biology (immunological and physiological response to infection) and spatial ecology (migratory performance of infected birds) will provide insights into the extent to which wild birds can act as vectors for AIV over long distances.
Immunology, Issue 54, migratory birds, active surveillance, lyophilized reagents, avian influenza, H5N1
Functional Mapping with Simultaneous MEG and EEG
Institutions: MGH - Massachusetts General Hospital.
We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.
JoVE neuroscience, Issue 40, neuroscience, brain, MEG, EEG, functional imaging
Quantifying Agonist Activity at G Protein-coupled Receptors
Institutions: University of California, Irvine, University of California, Chapman University.
When an agonist activates a population of G protein-coupled receptors (GPCRs), it elicits a signaling pathway that culminates in the response of the cell or tissue. This process can be analyzed at the level of a single receptor, a population of receptors, or a downstream response. Here we describe how to analyze the downstream response to obtain an estimate of the agonist affinity constant for the active state of single receptors.
Receptors behave as quantal switches that alternate between active and inactive states (Figure 1). The active state interacts with specific G proteins or other signaling partners. In the absence of ligands, the inactive state predominates. The binding of agonist increases the probability that the receptor will switch into the active state because its affinity constant for the active state (Kb
) is much greater than that for the inactive state (Ka
). The summation of the random outputs of all of the receptors in the population yields a constant level of receptor activation in time. The reciprocal of the concentration of agonist eliciting half-maximal receptor activation is equivalent to the observed affinity constant (Kobs
), and the fraction of agonist-receptor complexes in the active state is defined as efficacy (ε
) (Figure 2).
Methods for analyzing the downstream responses of GPCRs have been developed that enable the estimation of the Kobs
and relative efficacy of an agonist 1,2
. In this report, we show how to modify this analysis to estimate the agonist Kb
value relative to that of another agonist. For assays that exhibit constitutive activity, we show how to estimate Kb
in absolute units of M-1
Our method of analyzing agonist concentration-response curves 3,4
consists of global nonlinear regression using the operational model 5
. We describe a procedure using the software application, Prism (GraphPad Software, Inc., San Diego, CA). The analysis yields an estimate of the product of Kobs
and a parameter proportional to efficacy (τ
). The estimate of τKobs
of one agonist, divided by that of another, is a relative measure of Kb (RAi) 6
. For any receptor exhibiting constitutive activity, it is possible to estimate a parameter proportional to the efficacy of the free receptor complex (τsys
). In this case, the Kb
value of an agonist is equivalent to τKobs/τsys3
Our method is useful for determining the selectivity of an agonist for receptor subtypes and for quantifying agonist-receptor signaling through different G proteins.
Molecular Biology, Issue 58, agonist activity, active state, ligand bias, constitutive activity, G protein-coupled receptor
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Institutions: University of Washington, Iowa State University, North Carolina A&T University, Iowa Geological and Water Survey.
Finding the cost-efficient (i.e.
, lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.
) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25
. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7
with a multiobjective evolutionary algorithm SPEA226
, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.
Environmental Sciences, Issue 70, Plant Biology, Civil Engineering, Forest Sciences, Water quality, multiobjective optimization, evolutionary algorithms, cost efficiency, agriculture, development
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Institutions: University of California, Los Angeles.
Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy. Insight is given into how computational models can provide information on the population dynamics of mosquitoes and the spread of transposable elements through A. gambiae subspecies. The ethical considerations of releasing genetically modified mosquitoes into the wild are discussed.
Cellular Biology, Issue 5, mosquito, malaria, popuulation, replacement, modeling, infectious disease