A pinboard by
Omar Costilla-Reyes

Final year PhD student, University of Manchester


This Research aims to develop novel machine learning techniques for early detection of dementia

Alzheimer’s disease (AD) is a progressive, age-associated neurodegenerative disorder with a global prevalence likely to double every 20 years. There is no single test or biomarker that can detect or diagnose AD [Prince et al http://eprints.lse.ac.uk/67858/], yet predicting the earliest stages of cognitive impairment is vital to enable early intervention and possible disease modification. Currently, diagnosis relies on clinic-based assessment settings and diagnosis often occurs in the moderate or advanced stages of disease progression, making prevention or disease modification impossible.

Our aim is to develop novel data mining and analysis techniques based on a deep machine learning (DML) methodology, to ascertain early cognitive and functional decline indicative of degenerative brain disease from real life continuous, unobtrusively collected sensor data. DML techniques, which can detect abstract and complex patterns in large data sets, have demonstrated state-of-the-art performance in challenging artificial intelligence tasks, such as drug discovery [LeCun et al doi:10.1038/nature14539]. However, application of DML to early detection of AD from multimodal sensor data is not well studied.


Degradation of anthropogenic pollutant and organic dyes by biosynthesized silver nano-catalyst from Cicer arietinum leaves.

Abstract: The work represents the potent catalytic activity of silver nanoparticles synthesized from Cicer arietinum (chickpea) leaf extract (CAL-AgNPs). Here, silver nano-catalysts were used against the anthropogenic pollutants mainly involving nitro-amines and azo dyes. These pollutants are extremely harmful to our environment and causes severe health issues. The CAL-AgNPs have the potential to degrade harmful toxins and their by-products, thereby decreasing the pollutants from the environment. The green synthesis of nano-catalyst includes a simple, cost effective and eco-friendly method using the leaf extract from the plant. A systematic study was conducted, including synthesis, optimization and characterization of the silver particles. The AgNPs were further assessed through DLS and TEM for size and morphological evaluation. The obtained particles have shown spherical morphology with the size range of 88.8nm. Further, FTIR were performed for compositional and functional group analysis of the particles. The antibacterial efficiency was also evaluated against E. coli and P. aeruginosa. For their catalytic evaluation, CAL-AgNPs were assessed for 4-nitrophenol, methylene blue and congo red. The results obtained through catalytic evaluation suggested that the CAL-AgNPs could be helpful to surmount the environmental pollution in a very effective manner.

Pub.: 02 Aug '17, Pinned: 25 Aug '17