Autism Spectrum Disorder (ASD): From Molecular Mechanism to Novel Therapeutic Approach

Autism spectrum disorder (ASD) is the joint name for neurodevelopmental impairments characterized by abnormal social interaction, communication difficulties, limited range of activities and areas of interest, and typical motor impairments. There is a remarkable increase in the prevalence of ASD over the past 30 years. Studies indicate that genetic, neurological, and environmental factors are involved in the emergence of ASD, and recent works describe the neuromolecular mechanism implicated in the basis of ASD. 3LT has now developed into a therapeutic procedure that is used for three main goals: to reduce inflammation, edema, and chronic orthopedic disorders; to promote healing of wounds, deeper tissues, and nerves; and to treat neurological injuries and pain. 3LT may treat neurological injuries by lowering levels of inflammation proteins and by stimulation of mitochondria to increase the production of adenosine triphosphate and neural growth factors. This review aims to discuss the current evidence for the effects and mechanisms of 3LT at the cellular level and the effects of 3LT-induced changes in brain development and function. Early and effective intervention, through the developmental time window of high ASD susceptibility, using tools that are directed to the mechanism of pathology, may minimize neurological and functional deficits

INA Early Intervention for Babies at Risk

Brain and nervous system development are experience dependent. Indeed, the sequence of development is laid out genetically, but early environmental events are major contributors to the system’s development and optimal functioning. Various fetal injuries and birth trauma make babies vulnerable to developmental problems: cerebral palsy, seizures, abnormal muscle tone, delayed developmental milestones, sensory integration, and more. Our goal in the study presented here was to improve the neurodevelopmental track of babies at risk using Infant Neural Aquatic. Parent and baby dyads who met initial criteria were recruited for a 5–6 months intervention period through an open invitation, followed by a conversation and signing informed consent. In the beginning and end of intervention period, participants completed questionnaires, and developmental features of the babies were assessed using analysis of neuro-motor and vocal characteristics. Significant neurodevelopmental delta between values at the end and beginning of intervention period, comparing intervention and control, is described, and the strength of INA specific intervention tool is analyzed

Neuroplasticity in Young Age: Computer-Based Early Neurodevelopment Classifier

Neurodevelopmental syndromes, a continuously growing issue, are impairments in the growth and development of the brain and CNS which are pronounced in a variety of emotional, cognitive, motor and social skills. Early assessment and detection of typical, clinically correlated early signs of developmental abnormalities is crucial for early and effective intervention, supporting initiation of early treatment and minimizing neurological and functional deficits. Successful early interventions would then direct to early time windows of higher neural plasticity. Various syndromes are reflected in early vocal and motor characteristics, making them suitable indicators of an infant’s neural development. Performance of the computerized classifiers we developed shows approximately 90% accuracy on a database of diagnosed babies. The results demonstrate the potential of vocal and motor analysis for computer-assisted early detection of neurodevelopmental insults

A Correctness Condition for High-Performance Multiprocessors (Extended Abstract)

Hybrid consistency, a new consistency condition for shared memory multiprocessors, attempts to cap ture the guarantees provided by contemporary high-performance architectures. It combines the exprea-siveneas of strong consistency conditions (e.g., sequen-tial consistency, linearizability) and the efficiency of weak consistency conditions (e.g., Pipelined RAM, causal memory). Memory access operations are classified as either strong or weak. A global ordering of strong operations at different processes is guaran-teed, but there is very little guarantee on the ordering of weak operations at different processes, except for what is implied by their interleaving with the strong operations. A formal and precise definition of this condition is given. An efficient implementation of hy-brid consistency on distributed memory machines is presented. In this implementation, weak operations are executed instantaneously, while the response time for strong operations is linear in the network delay. (It is proven that this is within a constant factor of the optimal time bounds.) To motivate hybrid consistency it is shown that weakly consistent memories do not support non-cooperative (in particular, non-centralized) alg~ rithms for mutual exclusion

Order Statistics Approach to Estimation of the Dimension of the Noise Subspace

Model order selection and, in particular, determination of the dimension of the noise subspace, is an important problem in statistical signal processing. The discrete nature of the problem puts it in between detection and estimation. Standard tools from detection theory force a solution subject to arbitrary false alarm probability. On the other hand, direct maximum likelihood (ML) approach requires a penalty correction. In this paper we suggest the use of order statistics (OS) approach for the estimation of the dimension of the noise subspace. We show that the likelihood function of the ordered data has a unique non-trivial maximum with respect to the assumed dimension, and therefore we suggest an OS ML estimator. It is based on processing a single ordered sample and is, therefore, very simple. It assumes nothing about the distribution of the signal plus noise and therefore it is robust to the signal model. The suggested approach is demonstrated for i:i:d exponential noise

Assembling the Presynaptic Active Zone: A Characterization of an Active Zone Precursor Vesicle

The active zone is a specialized region of the presynaptic plasma membrane where synaptic vesicles dock and fuse. In this study, we have investigated the cellular mechanism underlying the transport and recruitment of the active zone protein Piccolo into nascent synapses. Our results show that Piccolo is transported to nascent synapses on an ∼80 nm dense core granulated vesicle together with other constituents of the active zone, including Bassoon, Syntaxin, SNAP-25, and N-cadherin, as well as chromogranin B. Components of synaptic vesicles, such as VAMP 2/synaptobrevin II, synaptophysin, synaptotagmin, or proteins of the perisynaptic plasma membrane such as GABA transporter 1 (GAT1), were not present. These studies demonstrate that the presynaptic active zone is formed in part by the fusion of an active zone precursor vesicle with the presynaptic plasma membrane