2019年

Visual impairment is a major global health problem. In 2017, WHO estimated that there were 253 million people worldwide with this ailment. According to the journal of the American Medical association, the prevalence of visual impairment in the Saudi population is 9.3%. Learning Braille by families of students with visual impairments remains a major obstacle, which precipitates several communication issues. Moreover, difficulties for the students themselves lie in learning braille with languages that include diacritical marks; consequently, affecting their academic progress. My main objective of this project is to help improving life quality of these individuals, and the focus is to advance their social productivity and adaptation. This was accomplished through creating a new simpler Arabic writing system using geometrical shapes. As a part of this project, fifteen participants with visual impairments were interviewed and tried this new writing system; two of them are adults between 25 and 40 years old while the rest are students from 9 to 17 years old. Additionally, 100 participants with visual impairments completed a survey. The data showed that students learned this system in two hours in comparison with students that mastered braille in a few months. This shows that this system is easier to learn and subsequently saves time and effort. The most important value added to this project is that diacritical marks were combined with the alphabet, thereby considerably reducing book sizes compared to Braille-written books. This project presents a novel system that helps people with visual impairments to increase their confidence and independence.

The cost of damage caused by hurricanes in 2017 is estimated to be over 200 billion dollars. Quick and accurate prediction of the path of a hurricane and its strength would be very valuable in alleviating these losses. Machine learning based prediction models, in contrast to models based on physics, have been developed successfully in many problem domains. A machine learning system infers the modeling function from a training dataset. This project developed machine learning based prediction models to forecast the path and strength of hurricanes in the North Atlantic basin. Feature analysis was performed on the HURDAT2 dataset, which contains paths and strengths of past hurricanes. Artificial Neural Networks (ANNs) and Generalized Linear Model (GLM) approaches such as Tikhonov regularization were investigated to develop nine hurricane prediction models. Prediction accuracy of these models was compared using a testing dataset, disjoint from the training dataset. The coefficient of determination and the mean squared error were used as performance metrics. Post-processing metrics, such as geodesic error in path prediction and the mean wind speed error, were also used to compare different models. TLS linear regression model performed the best of out the nine models for one and two time steps, while the ANNs made more accurate predictions for longer periods. All models predicted location and strength with greater than .95 coefficient of determination for up to two days. My models predicted hurricane path in under a second with accuracy comparable to that of current models.

Cancer is the second leading cause of death today[1], accounting for nearly 1 in 6 deaths worldwide. Despite this, diagnosis and treatment models for cancer are limited and as such, new methods to identify and treat susceptible patients are required urgently. HLAF- adjacent transcript 10 (FAT10) is an oncogene that is strongly implicated in the development of inflammation-associated cancers[2]. Previous research on this highly polymorphic gene has identified 2 haplotypes – the reference haplotype, which is found in both cancer patients and healthy individuals, as well as an additional haplotype that is occurs at higher frequency in cancer patients and is associated with higher odds of cancer. In this study, it was hypothesised that the cancer-associated FAT10 haplotype can better promote tumorigenicity and could thereby serve as a useful biomarker for cancer. Here, we functionally characterize the 2 FAT10 haplotypes to understand how they influence some of the hallmarks of cancer. The cancer-exclusive haplotype was observed to enhance hallmarks of cancer, namely uncontrolled cell growth, resisting cell death and anchorage-independent growth as compared to the reference haplotype. Moreover, we uncovered the differential gene expression patterns induced by each haplotype. Molecules involved in cell adhesion and proliferation, as well as transcription were upregulated by the cancer-associated haplotype and hence could have contributed to the increased tumourigenic potential of the cancer haplotype.

The 3D printer that we created is able to print objects out of concrete and is modular, so it can be assembled the way it is needed.

The synthesis of RNA using DNA (transcription) can be regulated by special proteins - transcription factors (TF) by binding to specific DNA regions - binding sites (BSTF). The purpose of the project is building a phylogenetic tree of orthologous groups of the each studying TF subfamilies, compare it with the tree of the corresponding BSTF motifs belonging to one orthologous group, find some common parts.

Nowadays Electric energy is the most useful in the world because we use it every day for lightening, work, entertainment ext … but electric energy also can be expensive and it will pollute the air plus we all know that the air pollution is getting worse. Our world consumes a huge amount of electric energy . Also we know that the homelessness is getting higher all around the globe and it reached a high percentage. The high price and the sudden cut of the electric energy and with it the air pollution makes a big problem. That’s why we created this project named TURN ON which is a friend of the environment and a friend of the humans. Our product will help us to produce and create strong, clean and renewable energy plus it will help the homeless to have free food and free transport tickets. After doing a lot of researches we found that our new method of producing energy gives a great electric energy and limit pollution. The kinetic energy is produced using rotations. That’s why we used the rotations of motorbikes, bicycles, cars wheels and turn that mechanical energy (wm) into electrical energy (we) that we can easily use in our daily life plus we can help homeless by giving them food widgets… in exchange with the electrical energy that they produced while using bicycles…After performing several tests and taking notes, we are able to conclude that our apparatus is indeed efficient as it is able to convert the rotation into electronic energy that we can store and use in emergencies to solve this big problem and in the same time to limit air pollution with using bicycles and reducing hunger regarding homeless. This machine should be easy to implement, cheap, does not depend on any other parameters such as the wind. Any rotation in any place can be a source of Electrical Energy. To facilitate the use of this new device, A START UP will be launched to rent electric bikes for “free”, distribute free food, snacks, tickets to homeless regarding to the energy production.

I built a robot that is able to improve safety in mines. The robot takes a series of sensor readings, do 3D mapping to compare deteriorating physical conditions in time, detect CO and CH4 levels and record video footage. All of this information is then sent back to the user. The project aims to build a robot that could decrease the amount of casualties in mines due to gas or instability. A strict engineering process, which comprised researching different features on a robot, was followed. A prototype robot was built, tested and improvements made. Some of the challenges faced, while building the prototype robot, included manoeuvrability over any type of terrain, even rough and rocky terrain. Choosing the correct driving mechanism (wheels, tracks, suspension and steering) also proved to be a very important feature that had to be kept in mind. The sensors used included, a temperature, humidity, carbon monoxide gas, as well as a methane gas sensor. A Gyro, Accelerometer and compass for easier navigation were also used. Two cameras which included a front camera for navigation and 3D mapping as well as a back camera for navigation were installed. The robot was tested over various terrains, it was able to retrieve sensor data and all of the engineering goals were reached. After the robot was built it was tested on various terrains. The robot achieved all of the engineering goals. The sensors was able to give readings, the robot 3D mapped an area and was also able to manoeuvre over rough terrain.

近年來中醫在慢性病的治療上已獲得很大的進展，許多中草藥的功能與特性皆是利用人體實驗來找出其效用，中藥內含的成分極為複雜，其功能與毒性測試常依賴於過去經驗醫學，許多中藥仍有待探討與實證，其對人體細胞和基因的影響仍不如西醫。因此目前國內外紛紛開始建置中草藥、成分、化合物相關數據庫供查詢，然而如何讓中醫的功能與毒性科學化變得是很重要的議題。本研究利用化學結構的數據分析來探討中藥成分與人體臟器與毒性的關係性。我們採用深度學習模型以中草藥的化學性質作為輸入，透過化學結構的圖像傳播，來預測中草藥相對應之臟器有效功能與毒性預測，希望透過本研究可以提供中藥對健康影響的依據並作為未來輔助中醫的工具，讓人們可更加了解食用中藥對人體可能有的正面與負面影響。

Antibacterial Properties of Mānuka Mānuka (Leptospermum scoparium) is a native 紐西蘭 plant that has long been used by indigenous Maori for its medicinal and therapeutic properties, yet is relatively unknown to science. Many of our native species may contain novel compounds with practical applications in our lives. Research indicates that mānuka has anti-microbial, anti-fungal, herbicidal, insecticidal and anti-bacterial properties, and suggests that compounds similar to Grandiflorone (ß-triketones) cause these effects. This project investigates the antibacterial properties of mānuka leaves, using the bacteria Photobacterium phosphoreum for biological testing. With the recent rise in multidrug-resistant bacteria, it is now more vital than ever to utilize traditional knowledge to inform research and development of innovative new antibiotics, antimicrobials and similar biologically important compounds.

本實驗提出了一個新的細胞化驗平台：結合螢光素酶和螢光奈米鑽石（Luciferase-Fluorescent Nanodiamond；Luc-FND）用來高靈敏檢測極少數量的細胞，克服人體間葉幹細胞（Mesenchymal Stem Cell; MSC）的數量稀少以及來源取得困難的問題。本實驗開發的Luc-FND assay，不同於以往的Luciferase assay，Luc-FND assay利用了FND當奈米載體（約為100nm），將裹上的螢光素酶送入細胞後監測細胞內冷光強度，用以得知細胞胞吞作用的多寡，進一步推算出細胞的存活率。本實驗將此機制應用於被不同濃度的化療用藥阿黴素（Doxorubicin；Dox）處理過的間葉幹細胞。結果顯示Luc-FND assay能夠高靈敏的檢測Dox對於間葉幹細胞的毒性，僅用1 ×103個細胞就能測出低至0.3125M的Dox劑量。本研究結果顯示，Luc-FND複合物是一種高效能的生醫工具，可將生物發光蛋白均質傳遞到間葉幹細胞中，提供了一種檢測和驗證治療成果的新方法。