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.
Beautiful Butterfly: The Physics Behind The Colors
Even as a child, I was fascinated by the colors in nature, such as rainbows, butterflies and flowers. This fascination developed into curiosity with age, and as my school studies developed, I became particularly interested in the scientific aspects of the origin and development of colors. I wanted to answer the question: How are the different colors of the butterfly wings related to the nanostructures of scales and pigments? The color on the butterfly wings results either from the pigmentation (chemical color) or from the structure (physical color) of the wing scales. Colors such as yellow, black, red and brown are mainly created by pigments. The interaction of light and structures in and on the surface of butterfly wings, often the size of the wavelength of the light, results in physical colors. These colors are usually bright and dependent on the viewing angle (unlike chemical pigments that spread light diffusely). The colors produced here are usually golden, green, purple and blue. But, where do these colors come from and why do certain species dazzle more than others? To get to the heart of the matter, I identified two key questions: • How are the different colors of the butterfly wings related to the nanostructures of scales and to the pigments? • Using the nanostructure, can you find out the wavelength of the reflected light? In this work, I focus on the structural colors of butterflies and study the physics behind them. This includes parachuting in areas such as diffraction gratings, scattering of light, interference in thin films, and multilayer interference. In order to experience the greatest possible diversity, I selected butterflies from different species for the measurements. Using the spectrometer, I measured the light reflected from butterflies. High-resolution microscopes such as the laser microscope and the scanning electron microscope gave me the opportunity to study the detailed nanostructures of the wing. In addition, I was able to analyze and evaluate my results using existing physical models and MATLAB simulations (Maxwell equations).
The expansion of ticks in the valley of Poschiavo: a growing threat to the future?
In recent years, the ticks have reached the valley of Poschiavo and so far no study has been done to determine their diffusion. Recently, this presence has become a much discussed topic as these ticks can be carriers of pathogenes and represent a danger to humans. The goal of this work is to analyze the current situation in the valley of Poschiavo to understand in which areas the ticks are widespread, if they are carriers of pathogens and which factors could have an influence on their expansion. Several methods have been used for data collection. Ticks were found on ungulates killed during the high hunt in autumn 2016. In spring 2017, ticks were collected in various areas of the valley using the flag method that involves dragging a cotton cloth onto the ground. Some of the collected ticks were sent to a laboratory to identify the presence of the Borrelia burgdorferi, the pathogen responsible for Lyme borreliosis. To understand the evolution of the presence of ticks in the valley, the doctors and veterinarians were interviewed. Finally, to identify any climate changes related to the diffusion of ticks, the evolution of the tem-perature and relative humidity measured by two meteorological stations in the valley of Poschiavo since 1980 have been analyzed. Thanks to this study it was possible to highlight for the first time the presence in the valley of Poschiavo of ticks wich are bearer of the Borrelia burgdorferi. In fact, the bacterium was present in 26% of the analyzed ticks. Currently, the thicks populate the southern part of the valley, from the lake of Poschiavo to Campocologno, a small area in the central part of the valley and the area around Poschiavo and San Carlo. The interviews carried out showed that in recent years the ticks in the valley have increased and that the climate change could be a possible cause. In fact, since 1980 the temperature measured on the bottom of the valley has increased on average by 1.5 ° C and also the relative humidity has risen slightly. These changes could affect the diffusion of ticks in the valley of Poschiavo. In the future the temperatures will rise further and consequently the climate of the Poschiavo valley will most likely be more suited to the life of the ticks favoring their in-crease.
"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.
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.
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.