Synthesis and Characterization of Glycol Chitosan DNA Nanoparticles for Retinal Gene Delivery

Given the number of monogenic ocular diseases and the number of non‐monogenic degenerative ocular diseases for which gene therapy is considered as a treatment, the development of effective therapeutic delivery strategies for DNA is a critical research goal. In this work, nonviral nanoparticles (NPs) composed of glycol chitosan (GCS) and plasmid DNA (pDNA) were generated, characterized, and evaluated. These particles are stable, do not aggregate in saline, are resistant to DNases, and have a hydrodynamic diameter of approximately 250 nm. Furthermore, the plasmid in these NPs was shown to maintain its proper conformation and can be released and expressed inside the cell. To determine whether these NPs would be suitable for intraocular use, pDNA carrying the ubiquitously expressed CBA‐eGFP expression cassette was compacted and subretinally injected into adult wild‐type albino mice. At day 14 post‐injection (PI), substantial green fluorescent protein (GFP) expression was observed exclusively in the retinal pigment epithelium (RPE) in eyes treated with GCS NPs but not in those treated with uncompacted pDNA or vehicle (saline). No signs of gross retinal toxicity were observed, and at 30 days PI, there was no difference in electroretinogram function between GCS NP‐, pDNA‐, or vehicle‐treated eyes. These results suggest that with further development, GCS NPs could be a useful addition to the available repertoire of genetic therapies for the treatment of RPE‐associated diseases.     

Prediction of Excess Air Factor in Automatic Feed Coal Burners by Processing of Flame Images

In this study, the relationship between the visual information gathered from the flame images and the excess air factor k in coal burners is investigated. In conventional coal burners the excess air factor k. can be obtained using very expensive air measurement instruments. The proposed method to predict k for a specific time in the coal burners consists of three distinct and consecutive stages; a) online flame images acquisition using a CCD camera, b) extraction meaningful information(flame intensity and brightness)from flame images, and c) learning these information(image features) with ANNs and estimate k. Six different feature extraction methods have been used: CDF of Blue Channel, Co-Occurrence Matrix, L_∞-Frobenius Norms,Radiant Energy Signal(RES), PCA and Wavelet. When compared prediction results, it has seen that the use of cooccurrence matrix with ANNs has the best performance(RMSE = 0.07) in terms of accuracy. The results show that the proposed predicting system using flame images can be preferred instead of using expensive devices to measure excess air factor in during combustion.     

Determining Attributes to Maximize Visibility of Objects

In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: How to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work well in practice. One type of exact algorithms is based on Integer Programming (IP) formulations of the problems. Another class of exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.     

Estimating vigilance level by using EEG and EMG signals

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.     

The Impact of Nanostructuring on the Thermal Conductivity of Thermoelectric CoSb3

The high concentration of grain boundaries provided by nanostructuring is expected to lower the thermal conductivity of thermoelectric materials, which favors an increase in their thermoelectric figure‐of‐merit, ZT. A novel chemical alloying method has been used for the synthesis of nanoengineered‐skutterudite CoSb₃. The CoSb₃ powders were annealed for different durations to obtain a set of samples with different particle sizes. The samples were then compacted into pellets by uniaxial pressing under various conditions and used for the thermoelectric characterization. The transport properties were investigated by measuring the Seebeck coefficient and the electrical and thermal conductivities in the temperature range 300 K to 650 K. A substantial reduction in the thermal conductivity of CoSb₃ was observed with decreasing grain size in the nanometer region. For an average grain size of 140 nm, the thermal conductivity was reduced by almost an order of magnitude compared to that of a single crystalline or highly annealed polycrystalline material. The highest ZT value obtained was 0.17 at 611 K for a sample with an average grain size of 220 nm. The observed decrease in the thermal conductivity with decreasing grain size is quantified using a model that combines the macroscopic effective medium approaches with the concept of the Kapitza resistance. The compacted samples exhibit Kapitza resistances typical of semiconductors and comparable to those of Si–Ge alloys.     

Optimization of the gain-bandwidth product of capacitive micromachined ultrasonic transducers

Capacitive micromachined ultrasonic transducers (cMUT) have large bandwidths, but they typically have low conversion efficiencies. This paper defines a performance measure in the form of a gain-bandwidth product and investigates the conditions in which this performance measure is maximized. A Mason model corrected with finite-element simulations is used for the purpose of optimizing parameters. There are different performance measures for transducers operating in transmit, receive, or pulse-echo modes. Basic parameters of the transducer are optimized for those operating modes. Optimized values for a cMUT with silicon nitride membrane and immersed in water are given. The effect of including an electrical matching network is considered. In particular, the effect of a shunt inductor in the gain-bandwidth product is investigated. Design tools are introduced, which are used to determine optimal dimensions of cMUTs with the specified frequency or gain response.     

Advanced modularity design for the MIT pebble bed reactor

The future of all reactors will depend on whether they can be economically built and operated. One of the major impediments to new nuclear construction is the capital cost due in large part to the length of construction time and complexity of the plant. Pebble bed reactors offer the opportunity to reduce the complexity of the plant because the number of safety systems required is significantly reduced due to the inherent safety of the technology. However, because of its small size, the capital cost per kilowatt is likely to be large if traditional construction approaches are followed. This strongly suggests the need for innovative construction concepts to reduce the construction time and cost. MIT has proposed a modularity approach in which the plant is pre-built in space-frame type modules which are built in factories. These space frames would contain all the equipment contained in a given volume. Once equipment in the space frame is installed, the space frame would then be shipped to the site and assembled “lego-style.” Studies presently underway have demonstrated the feasibility of the concept. Thermal stress analysis has been performed and an integrated design with the space frames has been developed. It is expected that this modularity approach will significantly shorten construction time and expense. This paper proposes a concept for further development, not a final design for the entire plant.     

IoHT-based deep learning controlled robot vehicle for paralyzed patients of smart cities

The Journal of Supercomputing - Paralysis caused by physical trauma is a common disease today, with approximately 30% of paralysis caused by this trauma. The disease in question both physically...