Long-lasting, drug-induced adaptations within the nucleus accumbens (NAc) have been proposed to contribute to drug-mediated addictive behaviors. Here we have used an optogenetic approach to examine the role of NAc medium spiny neurons (MSNs) expressing dopamine D2 receptors (D2Rs) in cocaine-induced behavioral sensitization. Adeno-associated viral vectors encoding channelrhodopsin-2 (ChR2) were delivered into the NAc of D2R-Cre transgenic mice. This allowed us to selectively photostimulate D2R-MSNs in NAc. D2R-MSNs form local inhibitory circuits, because photostimulation of D2R-MSN evoked inhibitory postsynaptic currents (IPSCs) in neighboring MSNs. Photostimulation of NAc D2R-MSN in vivo affected neither the initiation nor the expression of cocaine-induced behavioral sensitization. However, photostimulation during the drug withdrawal period attenuated expression of cocaine-induced behavioral sensitization. These results show that D2R-MSNs of NAc play a key role in withdrawal-induced plasticity and may contribute to relapse after cessation of drug abuse.

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Patient-to-medical image registration is a crucial factor that affects the accuracy of image-guided ENT- and neurosurgery systems. In this study, a novel registration protocol that extracts the point cloud in the patient space using the contact approach was proposed. To extract the optimal point cloud in patient space, we propose a multi-step registration protocol consisting of augmentation of the point cloud and creation of an optimal point cloud in patient space that satisfies the minimum distance from the point cloud in the medical image space. A hemisphere mathematical model and plastic facial phantom were used to validate the proposed registration protocol. An optical and electromagnetic tracking system, of the type that is commonly used in clinical practice, was used to acquire the point cloud in the patient space and evaluate the accuracy of the proposed registration protocol. The SRE and TRE of the proposed protocol were improved by about 30% and 50%, respectively, compared to those of a conventional registration protocol. In addition, TRE was reduced to about 28% and 21% in the optical and electromagnetic methods, respectively, thus showing improved accuracy. The new algorithm proposed in this study is expected to be applied to surgical navigation systems in the near future, which could increase the success rate of otolaryngological and neurological surgery.

Maintaining and monitoring the quality of eggs is a major concern during cold chain storage and transportation due to the variation of external environments, such as temperature or humidity. In this study, we proposed a deep learning-based Haugh unit (HU) prediction model which is a universal parameter to determine egg freshness using a non-destructively measured weight loss by transfer learning technique. The temperature and weight loss of eggs from a laboratory and real-time cold chain environment conditions are collected from ten different types of room temperature conditions. The data augmentation technique is applied to increase the number of the collected dataset. The convolutional neural network (CNN) and long short-term memory (LSTM) algorithm are stacked to make one deep learning model with hyperparameter optimization to increase HU value prediction performance. In addition, the general machine learning algorithms are applied to compare HU prediction results with the CNN-LSTM model. The source and target model for stacked CNN-LSTM used temperature and weight loss data, respectively. Predicting HU using only weight loss data, the target transfer learning CNN-LSTM showed RMSE value decreased from 6.62 to 2.02 compared to a random forest regressor, respectively. In addition, the MAE of HU prediction results for the target model decreased when the data augmentation technique was applied from 3.16 to 1.39. It is believed that monitoring egg freshness by predicting HU in a real-time cold chain environment can be implemented in real-life by using non-destructive weight loss parameters along with deep learning.

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