23Na chemical shift imaging in the living rat brain using a chemical shift agent,Tm[DOTP]5–

Objective

It is well known that the use of shift reagents (SRs) in nuclear magnetic resonance (NMR) studies is substantially limited by an intact blood–brain barrier (BBB). The current study aims to develop a method enabling chemical shift imaging in the living rat brain under physiological conditions using an SR, Tm[DOTP]⁵⁻.

Materials and methods

Hyperosmotic mannitol bolus injection followed by 60 min infusion of a Tm[DOTP]⁵⁻ containing solution was administered via a catheter inserted into an internal carotid artery. We monitored the homeostasis of physiological parameters, and we measured the thulium content in brain tissue post mortem using total reflection fluorescence spectroscopy (T-XRF). The alterations of the ²³Na resonance spectrum were followed in a 9.4T small animal scanner.

Results

Based on the T-XRF measurements, the thulium concentration was estimated at 2.3 ± 1.8 mM in the brain interstitial space. Spectroscopic imaging showed a split of the ²³Na resonance peak which became visible 20 min after starting the infusion. Chemical shift imaging revealed a significant decrease of the initial intensity level to 0.915 ± 0.058 at the end of infusion.

Conclusion

Our novel protocol showed bulk accumulation of Tm[DOTP]⁵⁻ thus enabling separation of the extra-/intracellular ²³Na signal components in the living rat brain while maintaining physiological homeostasis.

     

Element Transfer Investigations on Silica Based Submerged Arc Welding Fluxes

Silicon - By using laboratory developed agglomerated basic fluxes a study has been carried out to predict the element transfer across the series of bead on plate weld deposits in submerged arc...     

Image correlation technique for strain measurement of polycrystalline microstructures

An image processing technique is proposed to measure the deformation of polycrystalline materials based on correlating the grains in reference and deformed SEM images. The advantage of this technique compared to the conventional subset-based Digital Image Correlation (DIC) is that it can be applied when speckle patterning is not efficient or when studying boundary-related mechanics is the objective. The technique is based on correlating grains by defining their boundaries rather than just subsets of image pixels. It reveals the anisotropy inherent in the polycrystals since it allows the analysis to specify each grain separately without averaging the results. The technique is applied by detecting the approximate grain boundaries edges and then refining their location with high accuracy. The correlation is performed between points calculated from each grain in the reference and deformed images as a Point Set Registration (PSR) problem. Finally, the displacements and strains are calculated from the resulting transformation matrix. A benchmark problem was developed to discuss the error over a strain range of 0.02 to 0.2 and showed that the resulting strains are reasonably accurate. Also, an in situ experiment was conducted to demonstrate the implementation of the technique using a specimen with fine-grained Zirconia polycrystals. The technique successfully revealed the crack tip plastic zone, and strain mismatch between grains.     

Biologically Sensitive FETs: Holistic Design Considerations from Simulation,Modeling and Fabrication Perspectives

Silicon - The conceptualization of biomolecule sensing accomplished by field effect transistor (FET) devices have been attracting substantial contemplation for over twenty years owing to the...     

Feasible solution for dynamic economic load dispatch using feasible operation region

High penetration of renewable energy in the future power system will pose a big problem to the load dispatch operation. The large disturbance and high forecast error must be considered when scheduling a limited number of controllable generators to follow rapid change in load. This paper proposes a dynamic economic load dispatch (DELD) problem approach based on the concept of a feasible operation region (FOR). FOR is defined as the region that committed generators may operate in to match the load profile without violating the ramp‐rate constraints. The DELD problem is solved in two stages. In the first stage, FOR of each generator is computed using recent real‐time forecasted load as well as renewable energy generation. In the second stage, a generation schedule is determined by solving the DELD problem interval by interval while considering ramp‐rate constraints and FOR constraints. The method can gives feasible solution for feasible load and specify the amount of compensation required for feasible solution for infeasible load. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion-based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre-trained CNN model named EfficientNetB0 is fine-tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.     

Environmental stability of additively manufactured siliconized silicon carbide for applications in hybrid energy systems

A key consideration for the successful operation of hybrid energy systems will be the environmental stability of materials used for their construction, particularly when experiencing service environments containing water vapor at high temperatures. Here, we report results from the characterization of siliconized silicon carbide (Si-SiC) prepared via binder jet additive manufacturing and reactive silicon melt infiltration after being exposed to environments representative of those in solid oxide fuel cell (SOFC) anodes, and to exhaust gases inside a microturbine operating on natural gas. In both cases, it was found that oxide scales formed on the surface and that these scales were dense, continuous, and well-bonded to the substrates, although there was evidence of transverse and longitudinal cracking most likely as a result of mismatches in the thermal expansion of the scale and the substrate. Measured values of the thickness of the oxide scale were compared to those predicted by parabolic oxidation kinetics of silicon, but the potential effects of silica volatilization induced by water vapor, and silica reduction when exposed to hydrogen are discussed. The overall results showed that the oxide scale is expected to be protective under the conditions of hybrid power generation systems.     

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training, need for high resources, more training time, and difficulty in fine-tuning leading to performance issues. Taking a cue from the parallel processing found in the nature and its efficacy, we propose a lightweight ensemble based approach for solving the core RL tasks. It uses multiple binary action DQNs having shared state and reward. The benefits of the proposed approach are overall simplicity, faster convergence and better performance compared to conventional DQN based approaches. The approach can potentially be extended to any type of DQN by forming its ensemble. Conducting extensive experimentation, promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks, and Atari 2600 games as compared to recent techniques. The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task, 259.2 on the LunarLander-v2 task, and state-of-the-art results on four out of five Atari 2600 games.     

SE-MD: a single-encoder multiple-decoder deep network for point cloud reconstruction from 2D images

Pattern Analysis and Applications - 3D model reconstruction from single 2D RGB images is a challenging and actively researched computer vision task. Several techniques based on conventional network...