Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

New Insights into Molecular Mechanisms Underlying Neurodegenerative Disorders

Neurodegenerative disorders encompass a broad range of sporadic and/or familial debilitating conditions characterized by the progressive dysfunction and loss of selective neuronal populations, determining different clinical phenotypes. Emerging research data indicate an interplay of genetic factors and epigenetic mechanisms underlying neurodegenerative processes, which lead to increased prevalence of neurodegenerative disorders. In concert with the constant increase in the aging population, neurodegenerative disorders currently represent a major challenge to public health worldwide. Despite recent advances in clinical and preclinical research, the pathogenesis of these disorders still remains poorly understood, without effective treatments being available to halt the neurodegenerative processes, but rather aiming at relieving symptoms. Therefore, a critical evaluation of current research data and in-depth understanding of the molecular mechanisms that lead to neurodegeneration are crucial in order to identify potential therapeutic targets that can pave the way to the development of novel and promising therapies. This Special Issue is focused on novel molecular data in the field of neurodegeneration that associate with the onset and progression of neurodegenerative diseases. We are particularly interested in original articles and reviews that provide new insights into the main molecular pathogenic mechanisms underlying neurodegenerative disorders, aiming to identify potential biomarkers and novel therapeutic strategies

Deep Brain Stimulation (DBS) Applications

The issue is dedicated to applications of Deep Brain Stimulation and, in this issue, we would like to highlight the new developments that are taking place in the field. These include the application of new technology to existing indications, as well as ‘new’ indications. We would also like to highlight the most recent clinical evidence from international multicentre trials. The issue will include articles relating to movement disorders, pain, psychiatric indications, as well as emerging indications that are not yet accompanied by clinical evidence. We look forward to your expert contribution to this exciting issue

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page

Mathematical model of interactions immune system with Micobacterium tuberculosis

Tuberculosis (TB) remains a public health problem in the world, because of the increasing prevalence and treatment outcomes are less satisfactory. About 3 million people die each year and an estimated one third of the world's population infected with Mycobacterium Tuberculosis (M.tb) is latent. This is apparently related to incomplete understanding of the immune system in infection M.tb. When this has been known that immune responses that play a role in controlling the development of M.tb is Macrophages, T Lymphocytes and Cytokines as mediators. However, how the interaction between the two populations and a variety of cytokines in suppressing the growth of Mycobacterium tuberculosis germ is still unclear. To be able to better understand the dynamics of infection with M tuberculosis host immune response is required of a model.One interesting study on the interaction of the immune system with M.tb mulalui mathematical model approach. Mathematical model is a good tool in understanding the dynamic behavior of a system. With the mediation of mathematical models are expected to know what variables are most responsible for suppressing the growth of Mycobacterium tuberculosis germ that can be a more appropriate approach to treatment and prevention target is to develop a vaccine. This research aims to create dynamic models of interaction between macrophages (Macrophages resting, macrophages activated and macrophages infected), T lymphocytes (CD4 + T cells and T cells CD8 +) and cytokine (IL-2, IL-4, IL-10,IL-12,IFN-dan TNF-) on TB infection in the lung. To see the changes in each variable used parameter values derived from experimental literature. With the understanding that the variable most responsible for defense against Mycobacterium tuberculosis germs, it can be used as the basis for the development of a vaccine or drug delivery targeted so hopefully will improve the management of patients with tuberculosis. Mathematical models used in building Ordinary Differential Equations (ODE) in the form of differential equation systems Non-linear first order, the equation contains the functions used in biological systems such as the Hill function, Monod function, Menten- Kinetic Function. To validate the system used 4th order Runge Kutta method with the help of software in making the program Matlab or Maple to view the behavior and the quantity of cells of each population