Magnetic Resonance imaging (MRI) in detection of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeled with superparamagnetic iron oxide (SPIO) nanoparticle

*Purpose:* To investigate the MR imaging of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeling with superparamagnetic iron oxide (SPIO) nanoparticles.

*Materials and methods:* Tubes containing _B. longum_-SPIO, Free-SPIO, _B. longum_ and PYG Medium were incubated under anaerobic condition in _in vitro_ experiment. Transmission electron microscope and Prussian blue staining were used to demonstrate intra-bacteria nanoparticles. R~2~^*^ mapping and R~2~ mapping were reconstructed after MR scanning. _B. longum_-SPIO and _C. novyi_-NT-SPIO were injected respectively _in vivo_ to show whether it might be traced by MR imaging.

*Results:* Magnetosomes in bacteria were observed by electron microscopic and stained by Prussian blue staining. At the same concentration of SPIOs, the R~2~^*^ value of _B. longum_-SPIO was significantly higher than that of Free-SPIO (P<0.001), however, the R~2~ value was lower comparing with Free-SPIO (P<0.001). After injection with _B. longum_-SPIO, they could present in tumor and shorten T~2~^*^.

*Conclusion:* _B. longum_ and _C. novyi_-NT could be labeled by SPIO and then traced by MRI

Measles Rash Identification Using Residual Deep Convolutional Neural Network

Measles is extremely contagious and is one of the leading causes of vaccine-preventable illness and death in developing countries, claiming more than 100,000 lives each year. Measles was declared eliminated in the US in 2000 due to decades of successful vaccination for the measles. As a result, an increasing number of US healthcare professionals and the public have never seen the disease. Unfortunately, the Measles resurged in the US in 2019 with 1,282 confirmed cases. To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep convolutional neural network to distinguish measles rash from other skin conditions, in an aim to create a phone application in the future. On our image dataset, our model reaches a classification accuracy of 95.2%, sensitivity of 81.7%, and specificity of 97.1%, indicating the model is effective in facilitating an accurate detection of measles to help contain measles outbreaks

A Non-Iterative Balancing Method for HVAC Duct System

Building Heating, Ventilation and Air Conditioning (HVAC) system maintain comfortable indoor environment by supplying processed air to each terminal precisely through duct system. Testing, Adjusting and Balancing (TAB) plays critical role in achieving desired air distribution. Traditional TAB method is inaccurate and inefficient due to its trail-and-error natural, which forces people to pay high but expect low. Recently, it has been proposed that non-iterative approach to TAB is promising to improve performance and reduce cost. In this paper, a novel non-iterative balancing method is developed and implemented for TAB engineers to adjust dampers systematically and efficiently. Different from other TAB methods, this method is based on modeling and optimization. The mathematical model for duct system is firstly developed from its components including fan, duct segments and dampers to predict flow rates and pressures in the duct system for any damper positions. To identify the parameters in the model, flow rate measurements are taken for each terminal on real system under different damper positions. With the obtained model, optimal damper positions that gives desired air distribution are calculated by minimizing a specific objective function. To facilitate the adjusting process in real duct system, a sequential tuning instructions are generated which can help engineers to adjust dampers to their proper position using flowmeter as indicators. In this sequential tuning process, each damper only adjusts once to reach balance. Because the pressure and airflow dynamics of the duct system has been modeled, the entire TAB procedure is deterministic and non-iterative. Simulations are performed to validate the effectiveness of this method in Matlab/Simulink environment. Comparison study with existing methods shows that the proposed TAB method significantly shorten the duration of process and reduces balancing error while using easily-accessible equipment like pressure sensor and flowmeter only. It can be expected that the TAB service contractor will apply this method for advanced duct system where accurate air distribution is strictly required