GOALI: A Hybrid Method to Support Natural Interaction of Parts in a Virtual Environment

The overall goal of this research is to improve the design of assembly methods through the use of virtual reality (VR) and haptics (force feedback). The research is focused on two critical aspects: the development of methods for simulating natural part-to-part interaction to support the human-centric approach to concurrent design and the evaluation of these methods in a manufacturing design context. As part of the research, we have developed the SPARTA software platform, explored new VR interaction methods and conducted several site visits at Deere facilities to better understand their processes and operations. This paper summarizes the efforts and results to date

The Promise of Stochastic Resonance in Falls Prevention

Multisensory integration is essential for maintenance of motor and cognitive abilities, thereby ensuring normal function and personal autonomy. Balance control is challenged during senescence or in motor disorders, leading to potential falls. Increased uncertainty in sensory signals is caused by a number of factors including noise, defined as a random and persistent disturbance that reduces the clarity of information. Counter-intuitively, noise can be beneficial in some conditions. Stochastic resonance is a mechanism whereby a particular level of noise actually enhances the response of non-linear systems to weak sensory signals. Here we review the effects of stochastic resonance on sensory modalities and systems directly involved in balance control. We highlight its potential for improving sensorimotor performance as well as cognitive and autonomic functions. These promising results demonstrate that stochastic resonance represents a flexible and non-invasive technique that can be applied to different modalities simultaneously. Finally we point out its benefits for a variety of scenarios including in ambulant elderly, skilled movements, sports and to patients with sensorimotor or autonomic dysfunctions.Multisensory integration is essential for maintenance of motor and cognitive abilities, thereby ensuring normal function and personal autonomy. Balance control is challenged during senescence or in motor disorders, leading to potential falls. Increased uncertainty in sensory signals is caused by a number of factors including noise, defined as a random and persistent disturbance that reduces the clarity of information. Counter-intuitively, noise can be beneficial in some conditions. Stochastic resonance is a mechanism whereby a particular level of noise actually enhances the response of non-linear systems to weak sensory signals. Here we review the effects of stochastic resonance on sensory modalities and systems directly involved in balance control. We highlight its potential for improving sensorimotor performance as well as cognitive and autonomic functions. These promising results demonstrate that stochastic resonance represents a flexible and non-invasive technique that can be applied to different modalities simultaneously. Finally we point out its benefits for a variety of scenarios including in ambulant elderly, skilled movements, sports and to patients with sensorimotor or autonomic dysfunctions

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Quantitative 18F-AV1451 Brain Tau PET Imaging in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Patients

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results:18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification

Principal Axes of Inertia

Mechanics lawsSee how the eigenvectors of the inertia tensor change as you change a configuration of point masses, or the shape of a solid plate of materialComponente Curricular::Educação Superior::Ciências Exatas e da Terra::Físic

Developing a Person Centered Fear and Dementia (FaDe) assessment tool for individuals living with a dementia

Oral presentation at the PCE 2016: 12th World Association for Person Centered & Experiential Psychotherapy & Counseling conference, 20-24 July, New York City, USA

GOALI: A Hybrid Method to Support Natural Interaction of Parts in a Virtual Environment

The overall goal of this research is to improve the design of assembly methods through the use of virtual reality (VR) and haptics (force feedback). The research is focused on two critical aspects: the development of methods for simulating natural part-to-part interaction to support the human-centric approach to concurrent design and the evaluation of these methods in a manufacturing design context. As part of the research, we have developed the SPARTA software platform, explored new VR interaction methods and conducted several site visits at Deere facilities to better understand their processes and operations. This paper summarizes the efforts and results to date.This is a conference proceeding from 2011 NSF Engineering Research and Innovation (2011). Posted with permission.</p

The Learner Data Institute-Conceptualization: A Progress Report

This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem

The Learner Data Institute-Conceptualization: A Progress Report

This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem