FAT10 Haplotypes as a Potential Biomarker for Cancer
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.
"turn" -on (free food and renewable energy )
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.
Multiple Time-step Predictive Models for Hurricanes in the North Atlantic Basin Based on Machine Learning Algorithms
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.
Robotic Search and Rescue
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.