An Efficient Method to Improve the Audio Quality Using AAC Low Complexity Decoder

This paper presents a new approach to design a Digital Audio Broadcast (DAB) audio decoder is introduced to improve the superiority of audio. Countries all over the world use DAB broadcasting systems more prominently, in Europe. DAB+ is the upgraded version of digital audio broadcasting. DAB and DAB+ coexist in many countries, so receivers are essential to be compatible with both standards. DAB+ is approximately twice as efficient as DAB due to the adoption of the AAC+ audio codec, and DAB+ can provide high quality audio with bit rates as low as 64 kbit/s. Integrating an MPEG-1 Layer II (MP2) decoder and Advanced Audio Coding Low Complexity (AAC LC) decoder provides a fundamental audio decoding for DAB and DAB+. The generated audio frames data from the DAB channel decoders are stored in RAM. The bit stream demultiplexer parses the quantized spectrum data in the audio. The inverse quantization performs the inverse quantization computation and synthesis filter generates the time domain Pulse Code Modulation (PCM) samples, all the above operation results writes them back to the audio RAM. The existing system of this project uses HE AAC V2 decoder, that system consists has SBR and PS technologies. This two technologies are used to improve the sound quality in low bit rate program. The proposed scheme is uses AAC LC and MP2 decoder it improve the sound quality in high bit rate. The simulation of this project is carried out by using MATLAB R2011a and Xilinx ISE 9.2i. DOI: 10.17762/ijritcc2321-8169.15039

Encoding and retrieval in a CA1 microcircuit model of the hippocampus

Recent years have witnessed a dramatic accumulation of knowledge about the morphological, physiological and molecular characteristics, as well as connectivity and synaptic properties of neurons in the mammalian hippocampus. Despite these advances, very little insight has been gained into the computational function of the different neuronal classes; in particular, the role of the various inhibitory interneurons in encoding and retrieval of information remains elusive. Mathematical and computational models of microcircuits play an instrumental role in exploring microcircuit functions and facilitate the dissection of operations performed by diverse inhibitory interneurons. A model of the CA1 microcircuitry is presented using biophysical representations of its major cell types: pyramidal, basket, axo-axonic, bistratified and oriens lacunosummoleculare cells. Computer simulations explore the biophysical mechanisms by which encoding and retrieval of spatio-temporal input patterns are achieved by the CA1 microcircuitry. The model proposes functional roles for the different classes of inhibitory interneurons in the encoding and retrieval cycles

Promotion of Hendra virus replication by microRNA 146a

Hendra virus is a highly pathogenic zoonotic paramyxovirus in the genus Henipavirus. Thirty-nine outbreaks of Hendra virus have been reported since its initial identification in Queensland, Australia, resulting in seven human infections and four fatalities. Little is known about cellular host factors impacting Hendra virus replication. In this work, we demonstrate that Hendra virus makes use of a microRNA (miRNA) designated miR-146a, an NF-κB-responsive miRNA upregulated by several innate immune ligands, to favor its replication. miR-146a is elevated in the blood of ferrets and horses infected with Hendra virus and is upregulated by Hendra virus in human cells in vitro. Blocking miR-146a reduces Hendra virus replication in vitro, suggesting a role for this miRNA in Hendra virus replication. In silico analysis of miR-146a targets identified ring finger protein (RNF)11, a member of the A20 ubiquitin editing complex that negatively regulates NF-κB activity, as a novel component of Hendra virus replication. RNA interference-mediated silencing of RNF11 promotes Hendra virus replication in vitro, suggesting that increased NF-κB activity aids Hendra virus replication. Furthermore, overexpression of the IκB superrepressor inhibits Hendra virus replication. These studies are the first to demonstrate a host miRNA response to Hendra virus infection and suggest an important role for host miRNAs in Hendra virus disease

Investigation of the role of VHL-HIF signaling in DNA repair and apoptosis in zebrafish

pVHL is a tumor suppressor. The lack of its function leads to various tumors, among which ccRCC (clear cell renal cell carcinoma) has the most serious outcome due to its resistance to chemotherapies and radiotherapies. Although HIF promotes the progression of ccRCC, the precise mechanism by which the loss of VHL leads to tumor initiation remains unclear. We exploited two zebrafish vhl mutants, vhl and vll, and Tg (phd3:: EGFP)i144 fish to identify crucial functions of Vhl in tumor initiation. Through the mutant analysis, we found that the role of pVHL in DNA repair is conserved in zebrafish Vll. Interestingly, we also discovered that Hif activation strongly suppressed genotoxic stress induced DNA repair defects and apoptosis in vll and brca2 mutants and in embryos lacking ATM activity. These results suggest the potential of HIF as a clinical modulator that can protect cells from accumulating DNA damage and apoptosis which can lead to cancers and neurodegenerative disorders

Bidirectional crosstalk between hypoxia-inducible factor and glucocorticoid signalling in zebrafish larvae

In the last decades in vitro studies highlighted the potential for crosstalk between Hypoxia-Inducible Factor-(HIF) and glucocorticoid-(GC) signalling pathways. However, how this interplay precisely occurs in vivo is still debated. Here, we use zebrafish larvae (Danio rerio) to elucidate how and to what degree hypoxic signalling affects the endogenous glucocorticoid pathway and vice versa, in vivo. Firstly, our results demonstrate that in the presence of upregulated HIF signalling, both glucocorticoid receptor (Gr) responsiveness and endogenous cortisol levels are repressed in 5 days post fertilisation larvae. In addition, despite HIF activity being low at normoxia, our data show that it already impedes both glucocorticoid activity and levels. Secondly, we further analysed the in vivo contribution of glucocorticoids to HIF activity. Interestingly, our results show that both glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) play a key role in enhancing it. Finally, we found indications that glucocorticoids promote HIF signalling via multiple routes. Cumulatively, our findings allowed us to suggest a model for how this crosstalk occurs in vivo

Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors

Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing

Adoption of Hygienic Fish Handling Practices by Fishermen

ABSTRAC

Homeostatic Plasticity Studied Using In Vivo Hippocampal Activity-Blockade: Synaptic Scaling, Intrinsic Plasticity and Age-Dependence

Homeostatic plasticity is thought to be important in preventing neuronal circuits from becoming hyper- or hypoactive. However, there is little information concerning homeostatic mechanisms following in vivo manipulations of activity levels. We investigated synaptic scaling and intrinsic plasticity in CA1 pyramidal cells following 2 days of activity-blockade in vivo in adult (postnatal day 30; P30) and juvenile (P15) rats. Chronic activity-blockade in vivo was achieved using the sustained release of the sodium channel blocker tetrodotoxin (TTX) from the plastic polymer Elvax 40W implanted directly above the hippocampus, followed by electrophysiological assessment in slices in vitro. Three sets of results were in general agreement with previous studies on homeostatic responses to in vitro manipulations of activity. First, Schaffer collateral stimulation-evoked field responses were enhanced after 2 days of in vivo TTX application. Second, miniature excitatory postsynaptic current (mEPSC) amplitudes were potentiated. However, the increase in mEPSC amplitudes occurred only in juveniles, and not in adults, indicating age-dependent effects. Third, intrinsic neuronal excitability increased. In contrast, three sets of results sharply differed from previous reports on homeostatic responses to in vitro manipulations of activity. First, miniature inhibitory postsynaptic current (mIPSC) amplitudes were invariably enhanced. Second, multiplicative scaling of mEPSC and mIPSC amplitudes was absent. Third, the frequencies of adult and juvenile mEPSCs and adult mIPSCs were increased, indicating presynaptic alterations. These results provide new insights into in vivo homeostatic plasticity mechanisms with relevance to memory storage, activity-dependent development and neurological diseases

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small-molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small-molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared and openly published. CACHE will launch three new benchmarking exercises every year. The outcomes will be better prediction methods, new small-molecule binders for target proteins of importance for fundamental biology or drug discovery and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins. [Figure not available: see fulltext.

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience