Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in ...
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Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
We propose a new method of presenting two-dimensional information, such as figures and graphs, on a tactile display so that visually impaired people are able to perceive them quickly and accurately. The new presentati...
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We propose a new method of presenting two-dimensional information, such as figures and graphs, on a tactile display so that visually impaired people are able to perceive them quickly and accurately. The new presentation method is developed for a tactile-proprioceptive display, which can present information on not only conventional "concave-convex" tactile display, but also vibration presentation in arbitrary area on a tactile display and mechanical leading presentation by mechanically leading user's fingers using haptic device. In this paper, we outline the abovementioned two presentation method and the developed tactile-prop display, and objectively evaluate the effects of the local area vibration presentation method as an integral part of the tactile-prop display in comparison with the conventional "concave-convex" presentation method. We conducted experiments to evaluate the effects of the proposed local area vibration presentation method using two typical content patterns. In Experiment 1, discreetly dispersed objects are searched, and in Experiment 2, the cross graphs of line segments are distinguished and perceived. The experiments have proved that the method is effective in reducing search and cognitive time as well as identifying the correct cognition of cross graphs, as compared to the "concave-convex" tactile presentation method.
A massive amount of XML (Extensible Markup Language) data is available on the web, which can be viewed as tree data. One of the fundamental building blocks of information retrieval from tree data is answering structur...
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In human-robot teaming, interpretation of human actions, recognition of new situations, and appropriate decision making are crucial abilities for cooperative robots ("co-robots") to interact intelligently wi...
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ISBN:
(纸本)9781467380270
In human-robot teaming, interpretation of human actions, recognition of new situations, and appropriate decision making are crucial abilities for cooperative robots ("co-robots") to interact intelligently with humans. Given an observation, it is important that human activities are interpreted the same way by co-robots as human peers so that robot actions can be appropriate to the activity at hand. A novel interpretability indicator is introduced to address this issue. When a robot encounters a new scenario, the pretrained activity recognition model, no matter how accurate in a known situation, may not produce the correct information necessary to act appropriately and safely in new situations. To effectively and safely interact with people, we introduce a new generalizability indicator that allows a co-robot to self-reflect and reason about when an observation falls outside the co-robot's learned model. Based on topic modeling and the two novel indicators, we propose a new Self-reflective Risk-aware Artificial Cognitive (SRAC) model, which allows a robot to make better decisions by incorporating robot action risks and identifying new situations. Experiments both using real-world datasets and on physical robots suggest that our SRAC model significantly outperforms the traditional methodology and enables better decision making in response to human behaviors.
Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospit...
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Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospital room. The improvement of low performance using a single accelerometer at wrists and the inconvenience of sensor attached to a waist in the conventional approach was concentrated on by integrating heart rate signals to the conventional acceleration approach and changing the sensor location from a waist to wrists. As for the optimal heart rate feature selection, we proposed a four-feature vector combination (root mean square of successive differences, standard deviation of successive differences, normal to normal 50, normal to normal 20) with correlation and mutual information analysis in addition to mean absolute deviation selected as an accelerometer feature. To easily acquire and train the patients' fall data, our system was based on unsupervised learning approaches using Gaussian mixture models for optimal classifiers with the optimal cluster number decided by cluster validation index of square error sum. A 10-fold cross validation was applied for a final performance evaluation where each threshold for separating fall state from non-fall state was automatically decided in several comparison groups, which were created on the basis of fusion timing and used sensors. As a result, despite sensors attached to the wrist, the wearable inconvenience of the conventional is overcome using the feature-level fused approach between heart rates and accelerations with the accuracy up to 98.39 %, which is closest to 99.34 % of the case using a single accelerometer located at the waist.
Minimally invasive surgery prevents surgeons from manually palpating organs to locate subsurface tumors and other structures. One solution is to use ultrasound; however, it is not always reliable. Various minimally in...
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Minimally invasive surgery prevents surgeons from manually palpating organs to locate subsurface tumors and other structures. One solution is to use ultrasound; however, it is not always reliable. Various minimally invasive surgery instruments that provide tactile feedback have been proposed to augment ultrasound sensing for tumor localization; however, current designs have limitations such as cumbersome wiring, difficulty in manipulation, lack of sterilizability and high cost. This paper presents a novel, autoclavable, wireless, hand-held palpation instrument that uses a custom, low-cost, disposable tactile sensor to provide tactile and kinesthetic force feedback. The use of a replaceable, disposable tactile sensor avoids deterioration in sensor performance due to repeated autoclaving. The instrument features a passive joint in the end effector that allows the sensor to self-align to the palpation surface in a wide range of orientations. All of the electronics are packaged in a removable module that allows the rest of the instrument to be easily cleaned and autoclaved. Two versions of the tactile sensor, using piezoresistive sensing and capacitive sensing respectively, have been designed for use with this instrument. The instrument is shown to be able to detect 6 mm diameter spherical tumors at a depth of 9-10 mm in ex vivo tissue samples.
This is the second paper of a series authored by a collaboration of international researchers. It develops taxonomy for computer-based virtual reality simulators of relevance to the mining industry. A summary is prese...
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A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mits...
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni
The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, elect...
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ISBN:
(纸本)9781424479276
The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, electroencephalographic (EEG) changes in sensorimotor a-band at central electrodes, which desynchronizes both during execution and observation of goal-directed actions (i.e., μ suppression), have been considered an analog to MNS function. However, methodological and developmental issues, as well as the nature of generalized μ suppression to imagined, observed, and performed actions, have yet to provide a mechanistic relationship between EEG μ-rhythm and MNS function, and the extent to which EEG can be used to infer intent during MNS tasks remains unknown. In this study we present a novel methodology using active EEG and inertial sensors to record brain activity and behavioral actions from freely-behaving infants during exploration, imitation, attentive rest, pointing, reaching and grasping, and interaction with an actor. We used δ-band (1-4Hz) EEG as input to a dimensionality reduction algorithm (locality-preserving Fisher's discriminant analysis, LFDA) followed by a neural classifier (Gaussian mixture models, GMMs) to decode the each MNS task performed by freely-behaving 6-24 month old infants during interaction with an adult actor. Here, we present results from a 20-month male infant to illustrate our approach and show the feasibility of EEG-based classification of freely occurring MNS behaviors displayed by an infant. These results, which provide an alternative to the μ-rhythm theory of MNS function, indicate the informative nature of EEG in relation to intentionality (goal) for MNS tasks which may support action-understanding and thus bear implications for advancing the understanding of MNS function.
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