An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies

Prevalence of groundnut dry root rot (Macrophomina phaseolina (Tassi) Goid.) and its pathogenic variability in Southern India

Macrophomina phaseolina is the most devastating and emerging threat to groundnut production in India. An increase in average temperature and inconsistent rainfalls resulting from changing climatic conditions are strongly believed to aggravate the disease and cause severe yield losses. The present study aims to conduct a holistic survey to assess the prevalence and incidence of dry root rot of groundnut in major groundnut growing regions of Southern India, viz., Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu. Furthermore, the pathogenic variability was determined using different assays such as morphological, cultural, pathogenic, and molecular assays. Results indicate that disease incidence in surveyed locations ranged from 8.06 to 20.61%. Both temperature and rainfall played a major role in increasing the disease incidence. The pathogenic variability of M. phaseolina isolates differed significantly, based on the percent disease incidence induced on cultivars of JL-24 groundnut and K-6 groundnut. Morphological variations in terms of growth pattern, culture color, sclerotia number, and sclerotia size were observed. The molecular characterization of M. phaseolina isolates done by ITS rDNA region using ITS1 and ITS4 primers yielded approximately 600 bp PCR amplicons, sequenced and deposited in GenBank (NCBI). Molecular variability analysis using SSR primers indicated the genetic variation among the isolates collected from different states. The present investigation revealed significant variations in pathogenic variability among isolates of M. phaseolina and these may be considered important in disease management and the development of resistant cultivars against groundnut dry root rot disease

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Oracle ® Call Interface Programmer's Guide 11g Release 2 (11.2)

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