Photo-DSC and dynamic mechanical studies on UV curable compositions containing diacrylate of ricinoleic acid amide derived from castor oil

A difunctional acrylate of ricinoleic acid amide (COEDA) was synthesized from castor oil, which was non-irritant, odorless and capable of fast polymerization. Cross-linked networks were prepared by photo polymerization of the COEDA monomer as well as with varying amount of reactive diluents namely tripropylene glycol diacrylate (TPGDA) and trimethylol propane triacrylate (TMPTA) with the aim of modifying the curing and viscoelastic characteristics of the networks. Conversion as well as rate of conversion of COEDA copolymers enhanced with diluents percentage. Cure kinetics indicated an auto-accelerated process in which the rate constant increased with increasing amount of diluents. Storage modulus and glass transition temperature of UV-cured films were determined by DMTA. Films containing varying amount of TPGDA showed very close T_g-values where as those with increasing amount of TMPTA exhibited higher values of T_g.     

β2-Adrenoceptors in the Medial Prefrontal Cortex Excitatory Neurons Regulate Anxiety-like Behavior in Mice

The medial prefrontal cortex (mPFC) and β-adrenoceptors (βARs) have been implicated in modulating anxiety-like behavior. However, the specific contributions of the β2-AR subtype in mPFC in anxiety are still unclear. To address this issue, we used optogenetic and microRNA-based (miRNA) silencing to dissect the role of β2-AR in mPFC in anxiety-like behavior. On the one hand, we use a chimeric rhodopsin/β2-AR (Opto-β2-AR) with in vivo optogenetic techniques to selectively activate β2-adrenergic signaling in excitatory neurons of the mPFC. We found that opto-activation of β2-AR is sufficient to induce anxiety-like behavior and reduce social interaction. On the other hand, we utilize the miRNA silencing technique to specifically knock down the β2-AR in mPFC excitatory neurons. We found that the β2-AR knock down induces anxiolytic-like behavior and promotes social interaction compared to the control group. These data suggest that β2-AR signaling in the mPFC has a critical role in anxiety-like states. These findings suggest that inhibiting of β2-AR signaling in the mPFC may be an effective treatment of anxiety disorders.     

Chemogenetic Activation of Astrocytes in the Basolateral Amygdala Contributes to Fear Memory Formation by Modulating the Amygdala–Prefrontal Cortex Communication

The basolateral amygdala (BLA) is one of the key brain areas involved in aversive learning, especially fear memory formation. Studies of aversive learning in the BLA have largely focused on neuronal function, while the role of BLA astrocytes in aversive learning remains largely unknown. In this study, we manipulated the BLA astrocytes by expressing the Gq-coupled receptor hM3q and discovered that astrocytic Gq modulation during fear conditioning promoted auditorily cued fear memory but did not affect less stressful memory tasks or induce anxiety-like behavior. Moreover, chemogenetic activation of BLA astrocytes during memory retrieval had no effect on fear memory expression. In addition, astrocytic Gq activation increased c-Fos expression in the BLA and the medial prefrontal cortex (mPFC) during fear conditioning, but not in the home cage. Combining these results with retrograde virus tracing, we found that the activity of mPFC-projecting BLA neurons showed significant enhancement after astrocytic Gq activation during fear conditioning. Electrophysiology recordings showed that activating astrocytic Gq in the BLA promoted spike-field coherence and phase locking percentage, not only within the BLA but also between the BLA and the mPFC. Finally, direct chemogenetic activation of mPFC-projecting BLA neurons during fear conditioning enhanced cued fear memory. Taken together, our data suggest that astrocytes in the BLA may contribute to aversive learning by modulating amygdala–mPFC communication.     

Speech recognition using hybrid hidden markov model and NN classifier

This paper discusses the use of an integrated HMM/NN classifier for speech recognition. The proposed classifier combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN) classifier. Speech signals display a strong time varying characteristic. Although the neural net has been successful in many classification problems, its success (compared to HMM) is secondary to HMM in the field of speech recognition. The main reason is the lack of time normalization characteristics of most neural net structures (time-delay neural net is one notable exception but its structure is very complex). In the proposed integrated hybrid HMM/NN classifier, a left-to-right HMM module is used first to segment the observation sequence of every exemplar into a fixed number of states. Subsequently, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time scale variation, is transformed into a fixed number of frames, i.e., a static pattern. The multilayer perceptron (MLP) neural net is then used as the classifier for these time normalized exemplars. Some experimental results using telephone speech databases are presented to demonstrate the potential of this hybrid integrated classifier.     

RADIANT - hybrid multilayered chaotic image encryption system for color images

Multimedia Tools and Applications - Rapid growth in the use of digitized images in commercial, business, government and medical use has led to the need for protecting the images against...     

Low bandwidth data hiding for multimedia systems based on bit redundancy

Multimedia Tools and Applications - In recent years, low bandwidth data hiding schemes for multimedia systems are being seen as a promising new technology for multimedia information protection and...     

Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification

Neural Computing and Applications - One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was...     

Organic extract mediated synthesis of stable methylammonium lead bromide nanoparticles

Journal of Materials Science: Materials in Electronics - The growth of methylammonium lead bromide (CH3NH3PbBr3) nanoparticles using ligand-assisted Re-precipitation technique expending extract...