Discovery, Cloning and Recombinant Expression of a Coral Peptide with anti-Bacteria activity
Inflammatory Bowel Disease (IBD) is a prevalent disease of the West which pathogenesis is driven by a combination interaction between bacteria and inflammatory cells. In this study, two Kazal domain peptide from Palythoa Caribaeorum were identified. They were found to exhibit serine protease inhibitory, anti-bacterial effects and low toxicity, making them ideal candidates for IBD treatment due to their ability to inhibit inflammatory cell migration and bacterial load. We amplified their coding DNA sequences via PCR and ligated the resulting PCR product into pGEX-4T3 vector. The recombinant plasmid was verified by sequencing, and restriction digest before being transformed into competent E.coli cells. Following transformation, we induced target peptides expression by IPTG to confirmed successful transformation and peptide production. Selected transformed bacterial colonies were expanded in LB broth before mixing with glycerol and frozen in -80°C freezer to complete the process of cell bank production.
IlluminaMed: Developing Novel Artificial Intelligence Techniques for the Use In a Biomedical Image Analysis Toolkit and Personalized Medicine Engine
Despite the multitude of biomedical scans conducted, there is still relatively low accuracy and standardization of diagnoses from these images. In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. The aim of my research was automatic segmentation of brain MRI scans to better analyze patients with tumors, multiple sclerosis, ALS, or Alzheimer’s. In particular, I aim to use this information, along with novel artificial intelligence algorithms, to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. The result of the research was IlluminaMed, a biomedical image analysis toolkit that relies on the development of new artificial neural networks and training algorithms and novel research in fuzzy logic. The networks can detect patterns more complex than humans can identify and create patterns over long periods of time. IlluminaMed was trained by a dataset of professionally and manually segmented MRI scans from several prestigious hospitals and universities. I then developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the “rewards” are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. IlluminaMed can automatically segment the scans with 98% accuracy, find tumors with 96% accuracy and approximate their volume within a 2% margin of error. It can also find lesions in MS and ALS, distinguishing them from tumors with 94% accuracy. IlluminaMed can, in addition, determine the tendency of a patient to develop Alzheimer’s several months before patients develop symptoms correlating the brain structure and its fluctuations. Lastly, the censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored. IlluminaMed is the only fully automatic biomedical image analysis toolkit and personalized medicine engine. The personalized medicine engine runs at a level that is comparable to the best physicians. It is less computationally complex than similar software and is unique in the fact that it can find new patterns in the brain with possible future diagnoses. IlluminaMed’s implications are not only great in terms of the biomedical field, but also in the field of artificial intelligence with new findings in neural networks and the relationships of fuzzy extensional subsets.
Smart Washer
Data and records show accidents caused by loose bolts or nuts often occur in building or mechanical structures all over the world. They may be train derailments, parts falling off amusement rides, escalator breakdowns or wheels coming off automobiles. These incidents can often cause serious casualties and should not be ignored. At present, the only devices used to prevent screws loose are spring washer and nylon locking nuts, but they are not readily detectable with the naked eye when they failed to tighten. Based on simple mechanics and spring principled, our “Smart Washer” has been designed to detect loosen screws. Whenever the bolt or nut gets even slightly loose, the lower part of the washer will spring up, this is a sign to alert and remind the user to carry out maintenance and re-tighten the loosen screw before serious accidents occur.