Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

A preliminary study on real-time Rn/Tn discriminative detection using air-flow delay in two ion chambers in series

Due to its short half-life, thoron gas has been assumed to have negligible health hazards on humans compared to radon. But, one of the decay products with a long half-life can make it to be transported to a long distance and to cause a severe internal dose through respiration. Since most commercial radon detectors can not discriminate thoron signals from radon signals, it is very common to overestimate radon doses which in turn result in biased estimation of lung cancer risk in epidemiological studies. Though some methods had been suggested to measure thoron and radon separately, they could not be used for real-time measurement because of CR-39 or LR-115. In this study, an effective method was suggested to measure radon and thoron separately from the free air. It was observed that the activity of thoron decreases exponentially due to delay time caused by a long pipe between two chambers. Therefore from two ion chambers apart in time, it was demonstrated that thoron and radon could be measured separately and simultaneously. We also developed a collimated alpha source and with this source and an SBD, we could convert the ion chamber reading to count rate in cps

A device binding method based on content illumination pattern in public display environments.

Digital public displays installed in various locations provide valuable information for the passers-by. However, the static characteristic of the digital public display limits the consumption of the displayed content to a small area. Personal mobile devices such as smartphones are now capable of interacting with digital public displays, which enables the passers-by to "take-away" the content and consume it on-the-go. This process requires device binding, content selection, and transfer between the two devices. In this paper, we propose a device binding method which utilizes the content brightness changing pattern as a unique content ID on the public display and an illuminance sensor on the mobile to bind and transfer between two devices. We conducted performance evaluations for binding algorithm robustness in different conditions. Also comparative studies among other binding interaction methods were conducted. Our results show that our proposed method performed stably across the various conditions and overall performance in interaction completion time and error rate was similar or superior to the existing methods

Development of Chemiluminescence Resonance Test System Using SiPM Front-end ASIC to Detect Na and K Ions in Urine

The importance of measure and control dietary salinity arises to prevent and control the disease. There are several methods to measure the dietary salinities from blood or urine. The blood test is an accurate but inconvenient method because patients need to be at hospitals and wait for a longer time. Urine can be collected at home, and the test is more convenient. A 24-hour urine test is more accurate than random urine (RU) may cause more human errors. For this reason, testing RU accuracy for application will increase the convenience of patients. A SiPM sensor system to measure Guanine-based chemiluminescence resonance test light was developed. An ASIC system was developed and packaged to a chip. A test board for the packaged chip was developed. In parallel, the layout of an ASIC chip was assembled with SiPM and tested in the dark chamber to understand the functionality. The ASIC chip was tested in various frequencies with the test board. At the target frequency, the ASIC chip achieved 870 gain, which is exceeding the goal of 100. The SiPM system was measured with an oscilloscope, and the output signal was as expected. The performance test was done at a very high frequency (100MHz) and achieved 80.5% detection compared to the original light source signal. The ASIC chip development was successful, and SiPM matched the specification of the target operation