4,354 research outputs found
Do Alternative Therapies Have a Role in Autism?
Interventions considered to be branches of Complementary & Alternative Medicine (CAM) for autism are on the rise. Many new treatments have emerged & traditional beliefs of Ayurveda, Yoga, Behavioral therapy, Speech therapy and Homoeopathy have gained popularity and advocacy among parents. It is imperative that data supporting new treatments should be scrutinized for scientific study design, clinical safety, and scientific validity, before embarking on them as modes of therapy. Practitioners take care in explaining the rationale behind the various approaches that they practice, it is important to indicate possible limitations too during the initial clinical examination and interactive session. Clinicians must remember that parents may have different beliefs regarding the effectiveness of treatment since their information is derived more from the ‘hear-say’ route when they compare benefits/effects of CAM therapies on other children and often underestimate differential tolerance for treatment risks. It is thus significant that practitioners do not assume a "don't ask, don't tell" posture. The scientific validation and support for many interventions is incomplete and very different from the recommendations of the American Academy of Pediatrics Policy Statement. In this article, we discuss the various modes of CAM and their utilities and limitations in relation to autism
Assessment of Metabolic Parameters For Autism Spectrum Disorders
Autism is a brain development disorder that first appears during infancy or childhood, and generally follows a steady course without remission. Impairments result from maturation-related changes in various systems of the brain. Autism is one of the five pervasive developmental disorders (PDD), which are characterized by widespread abnormalities of social interactions and communication, and severely restricted interests and highly repetitive behavior. The reported incidence of autism spectrum disorders (ASDs) has increased markedly over the past decade. The Centre for Disease Control and Prevention has recently estimated the prevalence of ASDs in the United States at approximately 5.6 per 1000 (1 of 155 to 1 of 160) children. Several metabolic defects, such as phenylketonuria, are associated with autistic symptoms. In deciding upon the appropriate evaluation scheme a clinician must consider a host of different factors. The guidelines in this article have been developed to assist the clinician in the consideration of these factors
Metal oxide semiconductor nanomembrane-based soft unnoticeable multifunctional electronics for wearable human-machine interfaces
Wearable human-machine interfaces (HMIs) are an important class of devices that enable human and machine interaction and teaming. Recent advances in electronics, materials, and mechanical designs have offered avenues toward wearable HMI devices. However, existing wearable HMI devices are uncomfortable to use and restrict the human body's motion, show slow response times, or are challenging to realize with multiple functions. Here, we report sol-gel-on-polymer-processed indium zinc oxide semiconductor nanomembrane-based ultrathin stretchable electronics with advantages of multifunctionality, simple manufacturing, imperceptible wearing, and robust interfacing. Multifunctional wearable HMI devices range from resistive random-access memory for data storage to field-effect transistors for interfacing and switching circuits, to various sensors for health and body motion sensing, and to microheaters for temperature delivery. The HMI devices can be not only seamlessly worn by humans but also implemented as prosthetic skin for robotics, which offer intelligent feedback, resulting in a closed-loop HMI system
Representations of Double Affine Lie algebras
We study representations of the double affine Lie algebra associated to a
simple Lie algebra. We construct a family of indecomposable integrable
representations and identify their irreducible quotients. We also give a
condition for the indecomposable modules to be irreducible, this is analogous
to a result in the representation theory of quantum affine algebras. Finally,
in the last section of the paper, we show, by using the notion of fusion
product, that our modules are generically reducible
Recommended from our members
Photoluminescence Mapping over Laser Pulse Fluence and Repetition Rate as a Fingerprint of Charge and Defect Dynamics in Perovskites
Defects in metal halide perovskites (MHP) are photosensitive, making the observer effect unavoidable when laser spectroscopy methods are applied. Photoluminescence (PL) bleaching and enhancement under light soaking and recovery in dark are examples of the transient phenomena that are consequent to the creation and healing of defects. Depending on the initial sample composition, environment, and other factors, the defect nature and evolution can strongly vary, making spectroscopic data analysis prone to misinterpretations. Herein, the use of an automatically acquired dependence of PL quantum yield (PLQY) on the laser pulse repetition rate and pulse fluence as a unique fingerprint of both charge carrier dynamics and defect evolution is demonstrated. A simple visual comparison of such fingerprints allows for assessment of similarities and differences between MHP samples. The study illustrates this by examining methylammonium lead triiodide (MAPbI3) films with altered stoichiometry that just after preparation showed very pronounced defect dynamics at time scale from milliseconds to seconds, clearly distorting the PLQY fingerprint. Upon weeks of storage, the sample fingerprints evolve toward the standard stoichiometric MAPbI3 in terms of both charge carrier dynamics and defect stability. Automatic PLQY mapping can be used as a universal method for assessment of perovskite sample quality
H2G2-Net: A Hierarchical Heterogeneous Graph Generative Network Framework for Discovery of Multi-Modal Physiological Responses
Discovering human cognitive and emotional states using multi-modal
physiological signals draws attention across various research applications.
Physiological responses of the human body are influenced by human cognition and
commonly used to analyze cognitive states. From a network science perspective,
the interactions of these heterogeneous physiological modalities in a graph
structure may provide insightful information to support prediction of cognitive
states. However, there is no clue to derive exact connectivity between
heterogeneous modalities and there exists a hierarchical structure of
sub-modalities. Existing graph neural networks are designed to learn on
non-hierarchical homogeneous graphs with pre-defined graph structures; they
failed to learn from hierarchical, multi-modal physiological data without a
pre-defined graph structure. To this end, we propose a hierarchical
heterogeneous graph generative network (H2G2-Net) that automatically learns a
graph structure without domain knowledge, as well as a powerful representation
on the hierarchical heterogeneous graph in an end-to-end fashion. We validate
the proposed method on the CogPilot dataset that consists of multi-modal
physiological signals. Extensive experiments demonstrate that our proposed
method outperforms the state-of-the-art GNNs by 5%-20% in prediction accuracy.Comment: Paper accepted in Human-Centric Representation Learning workshop at
AAAI 2024 (https://hcrl-workshop.github.io/2024/
Markov chain aggregation and its application to rule-based modelling
Rule-based modelling allows to represent molecular interactions in a compact
and natural way. The underlying molecular dynamics, by the laws of stochastic
chemical kinetics, behaves as a continuous-time Markov chain. However, this
Markov chain enumerates all possible reaction mixtures, rendering the analysis
of the chain computationally demanding and often prohibitive in practice. We
here describe how it is possible to efficiently find a smaller, aggregate
chain, which preserves certain properties of the original one. Formal methods
and lumpability notions are used to define algorithms for automated and
efficient construction of such smaller chains (without ever constructing the
original ones). We here illustrate the method on an example and we discuss the
applicability of the method in the context of modelling large signalling
pathways
- …