Protein degradation and14C amino-acid incorporation rates into the foot muscle proteins of pond snailPila globosa during aestivation

Protein degradation and14C amino-acid incorporation rates in the foot muscle proteins of the pond snail,Pila globosa were studied with reference to aestivation. Lysosomal enzymes like: cathepsin, acid phosphatase except β-glucoronidase showed a decrease in activity on aestivation. Cathepsin activity showed an elevated temperature optimum on aestivation. Decreased proteolysis and autolysis on aestivation indicated a lowered turnover of proteins. To test this14C amino-acid incorporation rates were examined. Total proteins, myosin, actin, actomyosin, and tropomyosin did not exhibit any change in their incorporation rates. Sarcoplasmic proteins and collagen fraction decreased significantly in contrast to paramyosin on aestivation. It was concluded that aestivation resulted in changes in heterogeneous turnover of certain protein molecules

PROTEIN DEGRADATION AND 14C AMINO-ACID INCORPORATION RATES INTO THE FOOT MUSCLE PROTEINS OF POND SNAIL PILA GLOBOSA DURING AESTIVATION

Protein degradation and14C amino-acid incorporation rates in the foot muscle proteins of the pond snail,Pila globosa were studied with reference to aestivation. Lysosomal enzymes like: cathepsin, acid phosphatase except β-glucoronidase showed a decrease in activity on aestivation. Cathepsin activity showed an elevated temperature optimum on aestivation. Decreased proteolysis and autolysis on aestivation indicated a lowered turnover of proteins. To test this14C amino-acid incorporation rates were examined. Total proteins, myosin, actin, actomyosin, and tropomyosin did not exhibit any change in their incorporation rates. Sarcoplasmic proteins and collagen fraction decreased significantly in contrast to paramyosin on aestivation. It was concluded that aestivation resulted in changes in heterogeneous turnover of certain protein molecules

Progressive Faster Residual Convolutional Neural Network for Improving Osteoarthritis of the Temporomandibular Joint Detection

Osteoarthritis of the Temporomandibular Joint (TMJ-OA) is a chronic condition that affects the TMJ and is characterized by the progressive degeneration of the internal surfaces of the joint. Several deep learning models were adopted for identifying the TMJ-OA from the panoramic dental X-ray scans. Amongst, an Optimized Generative Adversarial Network (OGAN) with Faster Residual Convolutional Neural Network (FRCNN) produces more synthetic images to train the FRCNN for recognizing TMJ-OA cases. But, its accuracy was comparatively low while recognizing Region-of-Interest (RoI) from the panoramic scans that have analogous objects. Hence in this paper, an OGAN with a Progressive FRCNN (OGAN-PFRCNN) model is proposed, which enhances the FRCNN by integrating the Feature Pyramid Network (FPN) and RoI-grid attention strategy for TMJ-OA identification. First, the training images are fed to the ResNet101 for feature mining, which provides Multi-Scale Feature Map (MSFM) from the dental panoramic scans. Those features are then passed to the FPN with the RoI-grid attention strategy, which encodes richer characteristics by considering standard attention and graph-based point functions into a combined formulation. Then, those characteristics are fused at various levels to get a useful MSFM, which increases the network efficiency significantly. Moreover, such a Feature Map (FMap) is used to train the PFRCNN model, which is later applied to recognize the test scans into either healthy or TMJ-OA. At last, the testing outcomes show that the OGAN-PFRCNN attains 96.2% accuracy on the panoramic dental X-ray database compared to the FRCNN model

Postural Synergies and Their Development

The recent developments of a particular approach to analyzing motor synergies based on the principle of motor abundance has allowed a quantitative assessment of multieffector coordination in motor tasks involving anticipatory adjustments to self-triggered postural perturbations and in voluntary posturalsway. This approach, the uncontrolled manifold (UCM) hypothesis, is based on an assumption that the central nervous system organizes covariation of elemental variables to stabilize important performance variables in a task-specific manner. In particular, this approach has been used to demonstrate and to assess the emergence of synergies and their modification with motor practice in typical persons and persons with Down syndrome. The framework of the UCM hypothesis allows the formulation of testable hypotheses with respect to developing postural synergies in typically and atypically developing persons

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Motor Task Planning for Neuromuscular Function Tests using an Individual Muscle Control Technique

©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/ICORR.2009.5209522Presented at the 2009 IEEE 11th International Conference on Rehabilitation Robotics, Kyoto International Conference Center, Japan, June 23-26, 2009.A functionality test at the level of individual muscles may be effective for neuromuscular function tests. This paper proposes a novel computational method for neuromuscular function test planning using an individual muscle force control technique assisted by a rehabilitation robot. The algorithm will systematically compute an adequate amount and direction of force that a subject needs to exert, e.g., by his/her hand, to induce a desired muscle activation pattern of target muscle forces. A wearable robot with actuators (an exoskeleton robot, or a power-assisting device) is utilized to assist/resist the subject's joint torques. This paper presents a basic concept and preliminary simulation results. The simulation results justify the use of the wearable actuators in terms of the accuracy of muscle-level control during planned motor tasks

Individual Muscle Control using an Exoskeleton Robot for Muscle Function Testing

©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/TNSRE.2010.2047116Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable muscle-level force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named “individual muscle-force control” using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data