Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
Browse
Search Results
- Modeling the relationship between the gut microbiome and progressive neurodegenerative diseases: case study Alzheimer’s disease(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05-26) Trejo Castro, Alejandro Ismael; RANGEL ESCAREÑO, CLAUDIA; 200229; Rangel Escareño, Claudia; tolmquevedo; Alanis Funes, Gerardo Javier; Chávez Santoscoy, Rocío Alejandra; Fernández Figueroa, Edith Araceli; School of Engineering and Sciences; Campus MonterreyAlzheimer’s Disease (AD) has been known since 1906 and many of the symptoms and signs from the first case continue in the conceptualization of AD, such as memory loss, visuospatial disorders, impaired verbal communication, delirium, impotence and personality changes, such as depression and irritability, is the most common cause of dementia and neurodegenerative disease. It is expected to see an increment of up to 225% in the number of patients during a 40-year time frame (2010 - 2050). Clinically, the hallmark pathology of AD is the accumulation of amyloid-β (Aβ) protein fragments outside the neurons and accumulation of abnormal tau tangles within neurons. However, the microbiome composition is unique to a patient and, current studies have also proven the existence of a correlation with the microbiota that results in inflammation patterns and the accumulation of proteins related to AD. Nevertheless, no study so far has presented a model representing the interaction between the microbiota and the current tests to diagnose AD. In this study for the master’s program in Computer Science, we will approach a novel characterization of AD integrating clinical data, gut microbial metabolites and serum lipids metabolites. From a systems biology perspective, we intend to explain these covariates through machine learning and feature selection algorithms that would serve to find biomarkers between those who advance to the disease and those who does not. Data has been collected from various sources, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Alzheimer’s Disease Metabolomics Consortium (ADMC). Our findings suggest that the combination of gut microbial metabolites with the well-known neuropsychological tests could enhance the diagnosis and prediction of AD. This research project invite the researcher to carry out more experiments about the microbiome since we realized is becoming the key to better comprehend AD and probably other neurodegenerative diseases.
- ANOSCAR: An image captioning model and dataset designed from OSCAR and the video dataset of activitynet(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-07-01) Byrd Suárez, Emmanuel; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; puemcuervo; Ochoa Ruiz, Gilberto; Marín Hernandez, Antonio; School of Engineering and Sciences; Campus Estado de México; Chang Fernández, LeonardoActivity Recognition and Classification in video sequences is an area of research that has received attention recently. However, video processing is computationally expensive, and its advances have not been as extraordinary compared to those of Image Captioning. This work uses a computationally limited environment and learns an Image Captioning transformation of the ActivityNet-Captions Video Dataset that can be used for either Video Captioning or Video Storytelling. Different Data Augmentation techniques for Natural Language Processing are explored and applied to the generated dataset in an effort to increase its validation scores. Our proposal includes an Image Captioning dataset obtained from ActivityNet with its features generated by Bottom-Up attention and a model to predict its captions, generated with OSCAR. Our captioning scores are slightly better than those of S2VT, but with a much simpler pipeline, showing a starting point for future research using our approach, which can be used for either Video Captioning or Video Storytelling. Finally, we propose different lines of research to how this work can be further expanded and improved.