Absorption of Sr2+ at low concentrations using C.moniliferum-- With the aim of practical use of contaminated water processing of the Fukushima Daiichi Nuclear Power Station
We are conducting research for the purpose of treating contaminated water generated by the nuclear accident with C.moliniferum. In previous research, the school seniors examined whether there is a difference in absorption by changing the wavelength of the LED to establish efficient Sr2+ absorption conditions. As a result, the red wavelength was found to be effective for the efficient Sr2+ absorption of C. moniliferum. Therefore, in this study, in order to verify how much Sr is actually absorbed into the cell, the amount of Sr absorption using an atomic absorption photometer is quantified, and the previous research has shown that red is effective for the efficient Sr2+ absorption. The wavelength was considered to be effective because of photosynthesis, and was observed with a scanning electron microscope (SEM) using the photosynthesis inhibitor (DCMU). As a result, it was clarified that C. moniliferum absorbs Sr intracellularly, and photosynthesis was related to absorption.
Potential Diagnosis of Cancerous Cells Through Utilising Optical Spectroscopy
Cancer is responsible for an estimated 9.6 million deaths in 2018. Deaths from cancer worldwide are projected to reach over 13 million in 2030. Thus, developing a device that has the capability to solve today’s toughest global challenge is crucial by utilizing a simple yet robust approach - “SEEING THE UNSEEABLE” through bold innovation. Although removing cancer is much more effective than either radiation or chemotherapy, when unseen residual cancer cells remain, they could grow back into tumour overtime. The reoccurrence of cancer contributes to a greater risk of death. Hence, launching a system that is able to distinguish between the cancerous cell and normal cell is ultimately essential to make sure no cancer is left behind during surgery. This robust optical system is established with quantitative approach by exploring the integration of an algorithm into the developed software. The end result of this device has the capability to provide users an accurate numerical pH value. The developed system is integrated with the smart IoT gateway capability whereby this powerful analytical device is incorporated with the real-time monitoring, data transformation and data analyzer. Harnessing the power of technology lets us fight cancer better. Each time a pathologist analyzes tissue after operation, it can take up 2 to 3 days because the tissue has to be frozen, thinly sliced, and stained so it can be viewed under the microscope during the process of biopsy. Thus, it is crucial to invent this Surgeons’ VisionMetric device which has an IoT-based microcontroller that is capable of providing real-time numerical value on-site.
Improving Particle Classification In Wimp Dark Matter Detection Using Neural Networks
In all experiments for detection of WIMP dark matter, it is essential to develop a classifier that can distinguish potential WIMP events from background radiation. Most often, clas- sifiers are developed manually, via physical modeling and empirical optimization. This is problematic for two reasons: it takes a great deal of time and effort away from developing the experiment, and the resulting classifiers often perform suboptimally (which means that a greater amount of expensive run time is required to obtain a confident experimental result). Machine learning has the potential to automate this and accelerate experimentation, and also to detect patterns that humans cannot. However, two major challenges, which are shared among several dark matter experiments, stand in the way: impure calibration data, which hinders training of models, and unpredictable physical dynamics within the detector itself. My objective was to develop a set of machine learning techniques that address these two problems, and thus more efficiently generate highly accurate classifiers. I was able to obtain raw data for two dark matter experiments which exhibit these challenges: the PICO-60 bubble chamber [2], and the DEAP-3600 liquid argon scintillator [1]. For each experiment, I developed and compared three general-purpose algorithms intended to resolve its inherent challenge (impurity and unpredictable dynamics, respectively). In PICO-60, background alpha and WIMP-like neutron calibration datasets are used for training; however, there is an impurity of 10% alphas in the neutron set. While a conventional classifier was developed (and is believed to be 100% accurate), machine learning in the form of a supervised neural network (NN) has also been previously explored, because of the benefits of automation. Unfortunately, it achieved a mean accuracy of only 80.2% – not usable as a practical replacement for conventional methods in future iterations of the experiment. In DEAP-3600, photons are absorbed by a wavelength shifting medium and re-emitted in an unpredictable direction, before being detected by one of 255 photomultiplier tubes (PMTs) around the spherical detector. The randomness severely limits the accuracy of conventional classifiers; in a simulation, the best so far removes 99.6% of alpha background, while also (undesirably) removing 91.0% of WIMP events. Because of physical limitations, simulated data is used for calibration, with 30 real-world experimental events available for testing. I have written a research paper [11] about my work on PICO-60, which has been approved by the PICO collaboration and pre-published at https://arxiv.org/abs/1811.11308. It is currently undergoing peer review for publication in Computer Physics Communications. All PICO researchers are listed on my paper for their work on the original PICO-60 experi- ment. They did not contribute to this study; I completed and documented it independently.
DetectTimely
This research project focuses on developing a web-based multi-platform solution for augmenting prognostic strategies to diagnose breast cancer (BC), from a variety of different tests, including histology, mammography, cytopathology, and fine-needle aspiration cytology, all in an automated fashion. The respective application utilizes tensor-based data representations and deep learning architectural algorithms, to produce optimized models for the prediction of novel instances against each of these medical tests. This system has been designed in a way that all of its computation can be integrated seamlessly into a clinical setting, without posing any disruption to a clinician’s productivity or workflow, but rather an enhancement of their capabilities. This software can make the diagnostic process automated, standardized, faster, and even more accurate than current benchmarks achieved by both pathologists, and radiologists, which makes it invaluable from a clinical standpoint to make well-informed diagnostic decisions with nominal resources.
利用硫醇分子合成金奈米團簇應用於檢測自來水及游泳池水中次氯酸根
隨著大眾對於衛生要求的上升，許多抗菌及消毒成分被廣泛應用於水質處理中，其中次氯酸作為消毒殺菌劑大量使用於泳池及自來水的水質淨化中，然而現行標準方法測定水中有效氯所使用具危害的毒化物且步驟繁雜不利普及民生使用，發展簡便快速且靈敏的偵測方法勢在必行。本研究利用牛血清白蛋白（Bovine serum albumin, BSA）、不同的硫醇分子及金離子合成具螢光特性之硫醇修飾金奈米團簇 （Thiol ligand assists BSA capped gold nanoclusters, BSA/RSH-Au NCs），探討添加不同硫醇分子對所合成之金奈米團簇於不同pH值及常見離子對螢光強度之影響，並利用具有最佳螢光穩定性之2-巰基苯甲酸修飾金奈米團簇（Thiosalicylic acid assists BSA capped gold nanoclusters, BSA/TA-Au NCs），透析後進行次氯酸根檢測，其檢測線性範圍為0.98μM-1000μM，涵蓋法規規定游泳池水及自來水中次氯酸根之容許殘留濃度，最後此方法成功於游泳池水及自來水基質中檢測次氯酸根，分析樣品的回收率介於94.4%-95.6%。此外，在紙上添加金奈米團簇，並加入不同濃度的次氯酸根，觀察其螢光強度的變化，期望此方法未來應用於快篩試紙塗布材料快速檢測水質中次氯酸根濃度。