The Effects of the Circadian Rhythm on Nicotine-induced Neuronal Plasticity

Nicotine is a widely popular addictive drug that alters neural circuits in the brain. The resulting neuronal plasticity is varied by the inherent circadian rhythm. During the circadian rhythm, high levels of melatonin are present in the dark phase and low levels in the light phase. Two immediate early genes, Activity-regulated cytoskeleton (Arc) and Early growth response 1 (Egr1), were used as markers to analyze the effects of nicotine-induced plasticity in the light and dark phases. The results showed lower levels of nicotine-induced neuronal plasticity (both Arc and Egr1 present in lower levels) in dark nicotine groups than in light nicotine groups. This is because nicotine-induced Arc and Egr1 levels are suppressed in the presence of melatonin. Melatonin inhibits long term potentiation (LTP), which is an essential mechanism for neuronal plasticity (Wang et al, 2005). Hence the circadian rhythm does affect nicotine-induced neuronal plasticity by suppressing the effects of nicotine in the dark

Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

If online businesses possess valuable interest for suggesting their items by scoring them, then digital advertising gains their profits depending on their promotions or marketing task. Web users cannot be certain that the products handled via big-data recommendation are either advanced or interesting to their needs. In recent decades, recommender system models have been widely used to analyses large quantities of information. Amongst, a Distributed Improved Prediction with Matrix Factorization (MF) and Random Forest (RF) called DIPMF model exploits individual’s desires, choices and social context together for predicting the ratings of a particular item. But, the RF scheme needs high computation power and time for learning process. Also, its outcome was influenced by the training parameters. Hence this article proposes a Distributed Improved Deep Prediction with MF and ensemble learning (DIDPMF) model is proposed to decrease the computational difficulty of RF learning and increasing the efficiency of rating prediction. In this DIDPMF, a forest attribute extractor is ensemble with the Deep Neural Network (fDNN) for extracting the sparse attribute correlations from an extremely large attribute space. So, incorporating RF over DNN has the ability to provide prediction outcomes from all its base trainers instead of a single estimated possibility rate. This fDNN encompasses forest module and DNN module. The forest module is employed as an attribute extractor to extract the sparse representations from the given raw input data with the supervision of learning outcomes. First, independent decision trees are constructed and then ensemble those trees to obtain the forest. After, this forest is fed to the DNN module which acts as a learner to predict the individual’s ratings with the aid of novel attribute representations. Finally, the experimental results reveal that the DIDPMF outperforms than the other conventional recommender systems