Progress in gene therapy for neurological disorders

Diseases of the nervous system have devastating effects and are widely distributed among the population, being especially prevalent in the elderly. These diseases are often caused by inherited genetic mutations that result in abnormal nervous system development, neurodegeneration, or impaired neuronal function. Other causes of neurological diseases include genetic and epigenetic changes induced by environmental insults, injury, disease-related events or inflammatory processes. Standard medical and surgical practice has not proved effective in curing or treating these diseases, and appropriate pharmaceuticals do not exist or are insufficient to slow disease progression. Gene therapy is emerging as a powerful approach with potential to treat and even cure some of the most common diseases of the nervous system. Gene therapy for neurological diseases has been made possible through progress in understanding the underlying disease mechanisms, particularly those involving sensory neurons, and also by improvement of gene vector design, therapeutic gene selection, and methods of delivery. Progress in the field has renewed our optimism for gene therapy as a treatment modality that can be used by neurologists, ophthalmologists and neurosurgeons. In this Review, we describe the promising gene therapy strategies that have the potential to treat patients with neurological diseases and discuss prospects for future development of gene therapy

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Occurrence of the Brown Flycatcher (Musci capa Latirostris Raffles) in the Gir Forest

Volume: 63Start Page: 751End Page: 75

Barheaded and greylag geese in Gujarat

Volume: 85Start Page: 416End Page: 41

Distribution of the slenderbilled gull (Larus genei Breme) in the Gulf of Kachchh, Gujarat

Volume: 85Start Page: 420End Page: 42

Kalman based neural network analysis with resampling methods for longitudinal aerodynamic coefficient estimation

© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Accurateness of the approximated aerodynamic characteristics of an unstable aircraft is considerably significant in flight control system design or high-fidelity flight simulator development. Classical system identification technique consist of the equation and output error methods which are strongly influenced by data quality. Accordingly, unsatisfactory results may be arisen due to measurement or process noise. Consequently, most of the engineers rely on feed forward neural network in order to cope with those undesired drawbacks. But, noisy data dramatically degrades the performance of neural network as well; thus, the Kalman filter based backpropagation algorithm is proposed. Neural network approach has many parameters, including the hyperparameters, which have to be searched for an optimal result. To determine these optimal parameters, the genetic algorithm is used. In this paper, it is aimed to estimate longitudinal aerodynamic characteristics of highly maneuverable unstable aircraft with the engaged control system for flight simulation data. For this purpose, F-16 aircraft is modelled using the aerodynamic database derived from a low-speed wind tunnel test. Successful results show the effectiveness of the proposed method. To assess neural network approach performance, the most common resampling methods are used and compared in order to choose the best for each longitudinal aerodynamic coefficient