Minimal distance transformations between links and polymers: Principles and examples

The calculation of Euclidean distance between points is generalized to one-dimensional objects such as strings or polymers. Necessary and sufficient conditions for the minimal transformation between two polymer configurations are derived. Transformations consist of piecewise rotations and translations subject to Weierstrass-Erdmann corner conditions. Numerous examples are given for the special cases of one and two links. The transition to a large number of links is investigated, where the distance converges to the polymer length times the mean root square distance (MRSD) between polymer configurations, assuming curvature and non-crossing constraints can be neglected. Applications of this metric to protein folding are investigated. Potential applications are also discussed for structural alignment problems such as pharmacophore identification, and inverse kinematic problems in motor learning and control.Comment: Submitted to J. Phys.:Condens. Matte

Haptic guidance improves the visuo-manual tracking of trajectories

BACKGROUND: Learning to perform new movements is usually achieved by following visual demonstrations. Haptic guidance by a force feedback device is a recent and original technology which provides additional proprioceptive cues during visuo-motor learning tasks. The effects of two types of haptic guidances-control in position (HGP) or in force (HGF)-on visuo-manual tracking ("following") of trajectories are still under debate. METHODOLOGY/PRINCIPALS FINDINGS: Three training techniques of haptic guidance (HGP, HGF or control condition, NHG, without haptic guidance) were evaluated in two experiments. Movements produced by adults were assessed in terms of shapes (dynamic time warping) and kinematics criteria (number of velocity peaks and mean velocity) before and after the training sessions. CONCLUSION/SIGNIFICANCE: These results show that the addition of haptic information, probably encoded in force coordinates, play a crucial role on the visuo-manual tracking of new trajectories

A small number of abnormal brain connections predicts adult autism spectrum disorder

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum

A biologically inspired neural network controller for ballistic arm movements

<p>Abstract</p> <p>Background</p> <p>In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.</p> <p>Methods</p> <p>The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.</p> <p>Results</p> <p>The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.</p> <p>Curvature values are similar to those encountered in experimental measures with humans.</p> <p>The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.</p> <p>Conclusion</p> <p>The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.</p

Experimental Evidence of Time Delay Induced Death in Coupled Limit Cycle Oscillators

Experimental observations of time delay induced amplitude death in a pair of coupled nonlinear electronic circuits that are individually capable of exhibiting limit cycle oscillations are described. In particular, the existence of multiply connected death islands in the parameter space of the coupling strength and the time delay parameter for coupled identical oscillators is established. The existence of such regions was predicted earlier on theoretical grounds in [Phys. Rev. Lett. 80, 5109 (1998); Physica 129D, 15 (1999)]. The experiments also reveal the occurrence of multiple frequency states, frequency suppression of oscillations with increased time delay and the onset of both in-phase and anti-phase collective oscillations.Comment: 4 aps formatted RevTeX pages; 6 figures; to appear in Phys. Rev. Let

Reasons for Tooth Extractions in Japan: The Second Nationwide Survey

BACKGROUND: More than 10 years have passed since the first nationwide study on the reasons for tooth extraction in Japan. In the present study, we conducted the second nationwide survey to update the previous data. METHODS: This was a descriptive study. A sample population consisting of 5,250 dentists was selected by systematic random sampling using the 2018 membership directory of the Japan Dental Association. The reason for each permanent tooth extraction was documented by each dentist during a period of 1 week from June 4 to June 10, 2018. A questionnaire was provided for documentation. Reasons for tooth extraction were categorised into 6 groups as follows: caries, periodontal disease, fracture, orthodontics, impacted teeth, and others. RESULTS: A total of 2345 identified dentists responded to the questionnaire (recovery rate: 44.8%). Information on 7809 extracted teeth from 6398 patients was obtained. Periodontal disease was the main reason for tooth extraction for both sexes (men: 40.4%, women: 34.9%). Caries accounted for 30.2% of tooth extractions among men and 29.0% among women. Periodontal disease was predominant in the groups older than 55 years of age. Dental fracture accounted for 16.8% of tooth extractions among men and 19.2% among women. CONCLUSIONS: Caries and periodontal disease are still the main reasons for tooth extraction in Japan. Moreover, dentists should note that fractures accounted for approximately one-fifth of permanent tooth extractions after the age of 45 years

Self-Reported Diabetes Mellitus and Tooth Extraction Due to Periodontal Disease and Dental Caries in the Japanese Population

Diabetes mellitus is closely related to oral health. We aimed to determine the relationship between diabetes mellitus and tooth extraction due to periodontal disease and dental caries. Japan’s second nationwide survey data collected from 4 June to 10 June 2018 was used to identify reasons for tooth extraction among patients aged > 40 years. General dentists collected information on patients who underwent tooth extraction procedures, and the presence of diabetes mellitus was determined through interviews. Multivariable logistic regression was performed to investigate the relationship between diabetes mellitus and the reasons for tooth extraction, including periodontal disease and dental caries. In total, 2345 dentists responded to the survey (response rate 44.8%). We analyzed data on 4625 extracted teeth from 3750 patients (1815 males and 1935 females). Among patients with self-reported diabetes mellitus, 55.4% had extractions due to periodontal disease compared to 46.7% of such extractions among those without self-reported diabetes mellitus. Self-reported diabetes mellitus was significantly associated with tooth extraction due to periodontal disease. No significant differences were observed in dental caries according to self-reported diabetes mellitus status. This study provides further evidence of a significant association between diabetes mellitus and tooth extraction due to periodontal disease

The Binding of Learning to Action in Motor Adaptation

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.Alfred P. Sloan FoundationMcKnight Endowment Fund for Neuroscienc

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements.This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field