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Argumentation based joint learning: a novel ensemble learning approach.
PUBLISHED: 05-13-2015
Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.
In this interview, George Dimopoulos focuses on the physiological mechanisms used by mosquitoes to combat Plasmodium falciparum and dengue virus infections. Explanation is given for how key refractory genes, those genes conferring resistance to vector pathogens, are identified in the mosquito and how this knowledge can be used to generate transgenic mosquitoes that are unable to carry the malaria parasite or dengue virus.
21 Related JoVE Articles!
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A Proboscis Extension Response Protocol for Investigating Behavioral Plasticity in Insects: Application to Basic, Biomedical, and Agricultural Research
Authors: Brian H. Smith, Christina M. Burden.
Institutions: Arizona State University.
Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g., food, mates, threats). There are several behavioral mechanisms through which an insect learns salient associations and relates them to these events. It is important to understand this behavioral plasticity for programs aimed toward assisting insects that are beneficial for agriculture. This understanding can also be used for discovering solutions to biomedical and agricultural problems created by insects that act as disease vectors and pests. The Proboscis Extension Response (PER) conditioning protocol was developed for honey bees (Apis mellifera) over 50 years ago to study how they perceive and learn about floral odors, which signal the nectar and pollen resources a colony needs for survival. The PER procedure provides a robust and easy-to-employ framework for studying several different ecologically relevant mechanisms of behavioral plasticity. It is easily adaptable for use with several other insect species and other behavioral reflexes. These protocols can be readily employed in conjunction with various means for monitoring neural activity in the CNS via electrophysiology or bioimaging, or for manipulating targeted neuromodulatory pathways. It is a robust assay for rapidly detecting sub-lethal effects on behavior caused by environmental stressors, toxins or pesticides. We show how the PER protocol is straightforward to implement using two procedures. One is suitable as a laboratory exercise for students or for quick assays of the effect of an experimental treatment. The other provides more thorough control of variables, which is important for studies of behavioral conditioning. We show how several measures for the behavioral response ranging from binary yes/no to more continuous variable like latency and duration of proboscis extension can be used to test hypotheses. And, we discuss some pitfalls that researchers commonly encounter when they use the procedure for the first time.
Neuroscience, Issue 91, PER, conditioning, honey bee, olfaction, olfactory processing, learning, memory, toxin assay
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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
Authors: Fan Zhang, Ming Liu, Stephen Harper, Michael Lee, He Huang.
Institutions: North Carolina State University & University of North Carolina at Chapel Hill, University of North Carolina School of Medicine, Atlantic Prosthetics & Orthotics, LLC.
To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.
Biomedical Engineering, Issue 89, neural control, powered transfemoral prosthesis, electromyography (EMG), neural-machine interface, experimental setup and protocol
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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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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
Authors: Marc N. Coutanche, Sharon L. Thompson-Schill.
Institutions: University of Pennsylvania.
It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.
Neuroscience, Issue 89, fMRI, MVPA, connectivity, informational connectivity, functional connectivity, networks, multi-voxel pattern analysis, decoding, classification, method, multivariate
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In vivo Optogenetic Stimulation of the Rodent Central Nervous System
Authors: Michelle M. Sidor, Thomas J. Davidson, Kay M. Tye, Melissa R. Warden, Karl Diesseroth, Colleen A. McClung.
Institutions: University of Pittsburgh Medical Center, Stanford University, Massachusetts Institute of Technology, Cornell University, Stanford University.
The ability to probe defined neural circuits in awake, freely-moving animals with cell-type specificity, spatial precision, and high temporal resolution has been a long sought tool for neuroscientists in the systems-level search for the neural circuitry governing complex behavioral states. Optogenetics is a cutting-edge tool that is revolutionizing the field of neuroscience and represents one of the first systematic approaches to enable causal testing regarding the relation between neural signaling events and behavior. By combining optical and genetic approaches, neural signaling can be bi-directionally controlled through expression of light-sensitive ion channels (opsins) in mammalian cells. The current protocol describes delivery of specific wavelengths of light to opsin-expressing cells in deep brain structures of awake, freely-moving rodents for neural circuit modulation. Theoretical principles of light transmission as an experimental consideration are discussed in the context of performing in vivo optogenetic stimulation. The protocol details the design and construction of both simple and complex laser configurations and describes tethering strategies to permit simultaneous stimulation of multiple animals for high-throughput behavioral testing.
Neuroscience, Issue 95, optogenetics, rodent, behavior, opsin, channelrhodopsin, brain, fiber optics, laser, neural circuits
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Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees
Authors: Martin Fritz Brill, Maren Reuter, Wolfgang Rössler, Martin Fritz Strube-Bloss.
Institutions: University of Würzburg.
In both mammals and insects neuronal information is processed in different higher and lower order brain centers. These centers are coupled via convergent and divergent anatomical connections including feed forward and feedback wiring. Furthermore, information of the same origin is partially sent via parallel pathways to different and sometimes into the same brain areas. To understand the evolutionary benefits as well as the computational advantages of these wiring strategies and especially their temporal dependencies on each other, it is necessary to have simultaneous access to single neurons of different tracts or neuropiles in the same preparation at high temporal resolution. Here we concentrate on honeybees by demonstrating a unique extracellular long term access to record multi unit activity at two subsequent neuropiles1, the antennal lobe (AL), the first olfactory processing stage and the mushroom body (MB), a higher order integration center involved in learning and memory formation, or two parallel neuronal tracts2 connecting the AL with the MB. The latter was chosen as an example and will be described in full. In the supporting video the construction and permanent insertion of flexible multi channel wire electrodes is demonstrated. Pairwise differential amplification of the micro wire electrode channels drastically reduces the noise and verifies that the source of the signal is closely related to the position of the electrode tip. The mechanical flexibility of the used wire electrodes allows stable invasive long term recordings over many hours up to days, which is a clear advantage compared to conventional extra and intracellular in vivo recording techniques.
Neuroscience, Issue 89, honeybee brain, olfaction, extracellular long term recordings, double recordings, differential wire electrodes, single unit, multi-unit recordings
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Flying Insect Detection and Classification with Inexpensive Sensors
Authors: Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, Eamonn Keogh.
Institutions: University of California, Riverside, University of California, Riverside, University of São Paulo - USP, ISCA Technologies.
An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.
Bioengineering, Issue 92, flying insect detection, automatic insect classification, pseudo-acoustic optical sensors, Bayesian classification framework, flight sound, circadian rhythm
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Operant Procedures for Assessing Behavioral Flexibility in Rats
Authors: Anne Marie Brady, Stan B. Floresco.
Institutions: St. Mary's College of Maryland, University of British Columbia.
Executive functions consist of multiple high-level cognitive processes that drive rule generation and behavioral selection. An emergent property of these processes is the ability to adjust behavior in response to changes in one’s environment (i.e., behavioral flexibility). These processes are essential to normal human behavior, and may be disrupted in diverse neuropsychiatric conditions, including schizophrenia, alcoholism, depression, stroke, and Alzheimer’s disease. Understanding of the neurobiology of executive functions has been greatly advanced by the availability of animal tasks for assessing discrete components of behavioral flexibility, particularly strategy shifting and reversal learning. While several types of tasks have been developed, most are non-automated, labor intensive, and allow testing of only one animal at a time. The recent development of automated, operant-based tasks for assessing behavioral flexibility streamlines testing, standardizes stimulus presentation and data recording, and dramatically improves throughput. Here, we describe automated strategy shifting and reversal tasks, using operant chambers controlled by custom written software programs. Using these tasks, we have shown that the medial prefrontal cortex governs strategy shifting but not reversal learning in the rat, similar to the dissociation observed in humans. Moreover, animals with a neonatal hippocampal lesion, a neurodevelopmental model of schizophrenia, are selectively impaired on the strategy shifting task but not the reversal task. The strategy shifting task also allows the identification of separate types of performance errors, each of which is attributable to distinct neural substrates. The availability of these automated tasks, and the evidence supporting the dissociable contributions of separate prefrontal areas, makes them particularly well-suited assays for the investigation of basic neurobiological processes as well as drug discovery and screening in disease models.
Behavior, Issue 96, executive function, behavioral flexibility, prefrontal cortex, strategy shifting, reversal learning, behavioral neuroscience, schizophrenia, operant
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The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
Authors: Robert D. Kirch, Richard C. Pinnell, Ulrich G. Hofmann, Jean-Christophe Cassel.
Institutions: University Hospital Freiburg, UMR 7364 Université de Strasbourg, CNRS, Neuropôle de Strasbourg.
Spatial cognition research in rodents typically employs the use of maze tasks, whose attributes vary from one maze to the next. These tasks vary by their behavioral flexibility and required memory duration, the number of goals and pathways, and also the overall task complexity. A confounding feature in many of these tasks is the lack of control over the strategy employed by the rodents to reach the goal, e.g., allocentric (declarative-like) or egocentric (procedural) based strategies. The double-H maze is a novel water-escape memory task that addresses this issue, by allowing the experimenter to direct the type of strategy learned during the training period. The double-H maze is a transparent device, which consists of a central alleyway with three arms protruding on both sides, along with an escape platform submerged at the extremity of one of these arms. Rats can be trained using an allocentric strategy by alternating the start position in the maze in an unpredictable manner (see protocol 1; §4.7), thus requiring them to learn the location of the platform based on the available allothetic cues. Alternatively, an egocentric learning strategy (protocol 2; §4.8) can be employed by releasing the rats from the same position during each trial, until they learn the procedural pattern required to reach the goal. This task has been proven to allow for the formation of stable memory traces. Memory can be probed following the training period in a misleading probe trial, in which the starting position for the rats alternates. Following an egocentric learning paradigm, rats typically resort to an allocentric-based strategy, but only when their initial view on the extra-maze cues differs markedly from their original position. This task is ideally suited to explore the effects of drugs/perturbations on allocentric/egocentric memory performance, as well as the interactions between these two memory systems.
Behavior, Issue 101, Double-H maze, spatial memory, procedural memory, consolidation, allocentric, egocentric, habits, rodents, video tracking system
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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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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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Operant Learning of Drosophila at the Torque Meter
Authors: Bjoern Brembs.
Institutions: Free University of Berlin.
For experiments at the torque meter, flies are kept on standard fly medium at 25°C and 60% humidity with a 12hr light/12hr dark regime. A standardized breeding regime assures proper larval density and age-matched cohorts. Cold-anesthetized flies are glued with head and thorax to a triangle-shaped hook the day before the experiment. Attached to the torque meter via a clamp, the fly's intended flight maneuvers are measured as the angular momentum around its vertical body axis. The fly is placed in the center of a cylindrical panorama to accomplish stationary flight. An analog to digital converter card feeds the yaw torque signal into a computer which stores the trace for later analysis. The computer also controls a variety of stimuli which can be brought under the fly's control by closing the feedback loop between these stimuli and the yaw torque trace. Punishment is achieved by applying heat from an adjustable infrared laser.
Neuroscience, Issue 16, operant, learning, Drosophila, fruit fly, insect, invertebrate, neuroscience, neurobiology, fly, conditioning
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Morris Water Maze Experiment
Authors: Joseph Nunez.
Institutions: Michigan State University (MSU).
The Morris water maze is widely used to study spatial memory and learning. Animals are placed in a pool of water that is colored opaque with powdered non-fat milk or non-toxic tempera paint, where they must swim to a hidden escape platform. Because they are in opaque water, the animals cannot see the platform, and cannot rely on scent to find the escape route. Instead, they must rely on external/extra-maze cues. As the animals become more familiar with the task, they are able to find the platform more quickly. Developed by Richard G. Morris in 1984, this paradigm has become one of the "gold standards" of behavioral neuroscience.
Behavior, Issue 19, Declarative, Hippocampus, Memory, Procedural, Rodent, Spatial Learning
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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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Cross-Modal Multivariate Pattern Analysis
Authors: Kaspar Meyer, Jonas T. Kaplan.
Institutions: University of Southern California.
Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices.
Neuroscience, Issue 57, perception, sensory, cross-modal, top-down, mental imagery, fMRI, MRI, neuroimaging, multivariate pattern analysis, MVPA
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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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Appetitive Associative Olfactory Learning in Drosophila Larvae
Authors: Anthi A. Apostolopoulou, Annekathrin Widmann, Astrid Rohwedder, Johanna E. Pfitzenmaier, Andreas S. Thum.
Institutions: University of Konstanz, University of Fribourg.
In the following we describe the methodological details of appetitive associative olfactory learning in Drosophila larvae. The setup, in combination with genetic interference, provides a handle to analyze the neuronal and molecular fundamentals of specifically associative learning in a simple larval brain. Organisms can use past experience to adjust present behavior. Such acquisition of behavioral potential can be defined as learning, and the physical bases of these potentials as memory traces1-4. Neuroscientists try to understand how these processes are organized in terms of molecular and neuronal changes in the brain by using a variety of methods in model organisms ranging from insects to vertebrates5,6. For such endeavors it is helpful to use model systems that are simple and experimentally accessible. The Drosophila larva has turned out to satisfy these demands based on the availability of robust behavioral assays, the existence of a variety of transgenic techniques and the elementary organization of the nervous system comprising only about 10,000 neurons (albeit with some concessions: cognitive limitations, few behavioral options, and richness of experience questionable)7-10. Drosophila larvae can form associations between odors and appetitive gustatory reinforcement like sugar11-14. In a standard assay, established in the lab of B. Gerber, animals receive a two-odor reciprocal training: A first group of larvae is exposed to an odor A together with a gustatory reinforcer (sugar reward) and is subsequently exposed to an odor B without reinforcement 9. Meanwhile a second group of larvae receives reciprocal training while experiencing odor A without reinforcement and subsequently being exposed to odor B with reinforcement (sugar reward). In the following both groups are tested for their preference between the two odors. Relatively higher preferences for the rewarded odor reflect associative learning - presented as a performance index (PI). The conclusion regarding the associative nature of the performance index is compelling, because apart from the contingency between odors and tastants, other parameters, such as odor and reward exposure, passage of time and handling do not differ between the two groups9.
Neuroscience, Issue 72, Developmental Biology, Neurobiology, Biochemistry, Molecular Biology, Physiology, Behavior, Drosophila, fruit fly, larvae, instar, olfaction, olfactory system, odor, 1-octanol, OCT, learning, reward, sugar, feeding, animal model
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Authors: Marcus Cheetham, Lutz Jancke.
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 (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.
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
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Movement Retraining using Real-time Feedback of Performance
Authors: Michael Anthony Hunt.
Institutions: University of British Columbia .
Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular patterns responsible for governing the movement performance. This motor learning can be enhanced through a number of methods that are utilized in research and clinical settings alike. In general, verbal feedback of performance in real-time or knowledge of results following movement is commonly used clinically as a preliminary means of instilling motor learning. Depending on patient preference and learning style, visual feedback (e.g. through use of a mirror or different types of video) or proprioceptive guidance utilizing therapist touch, are used to supplement verbal instructions from the therapist. Indeed, a combination of these forms of feedback is commonplace in the clinical setting to facilitate motor learning and optimize outcomes. Laboratory-based, quantitative motion analysis has been a mainstay in research settings to provide accurate and objective analysis of a variety of movements in healthy and injured populations. While the actual mechanisms of capturing the movements may differ, all current motion analysis systems rely on the ability to track the movement of body segments and joints and to use established equations of motion to quantify key movement patterns. Due to limitations in acquisition and processing speed, analysis and description of the movements has traditionally occurred offline after completion of a given testing session. This paper will highlight a new supplement to standard motion analysis techniques that relies on the near instantaneous assessment and quantification of movement patterns and the display of specific movement characteristics to the patient during a movement analysis session. As a result, this novel technique can provide a new method of feedback delivery that has advantages over currently used feedback methods.
Medicine, Issue 71, Biophysics, Anatomy, Physiology, Physics, Biomedical Engineering, Behavior, Psychology, Kinesiology, Physical Therapy, Musculoskeletal System, Biofeedback, biomechanics, gait, movement, walking, rehabilitation, clinical, training
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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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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
Authors: Barbara Sargent, Hendrik Reimann, Masayoshi Kubo, Linda Fetters.
Institutions: University of Southern California, Temple University, Niigata University of Health and Welfare.
Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants’ leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.
Behavior, Issue 100, infant, discovery-learning, motor learning, motor control, kinematics, kinetics
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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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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.