Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.

"Predicting the future isn't magic, it's artificial intelligence" Dave Waters. In the last decades there has been an unprecedented growth in the field of machine learning, and particularly within deep learning models. The combination of big data and computational power has nurtured the evolution of a variety of new methods to predict and interpret future scenarios. These data centric models can achieve exceptional performances on specific tasks, with their prediction boundaries continuously expanding towards new and more complex challenges. However, the model complexity often translates into a lack of interpretability from a scientific c perspective, it is not trivial to identify the factors involved in final outcomes. Explainability may not always be a requirement for some machine learning tasks, specially when it comes in detriment of performance power. But for some applications, such as biological discoveries or medical diagnostics, understanding the output and determining factors that influence decisions is essential. In this thesis we develop both a supervised and unsupervised approach to map from genotype to phenotype. We emphasise the importance of interpretability and feature extraction from the models, by identifying relevant genes for cell differentiation. We then continue to explore the rules and mechanisms behind the models from a theoretical perspective. Using information theory to explain the learning process and applying perturbation theory to transform the results into a generalisable representation. We start by building a supervised approach to mapping cell profiles from genotype to phenotype, using single cell RNA-Seq data. We leverage non-linearities among gene expressions to identify cellular levels of differentiation. The ambiguity and even absence of labels in most biological studies instigated the development of novel unsupervised techniques, leading to a new general and biologically interpretable framework based on Variational Autoencoders. The application and validation of the methods has proven to be successful, but questions regarding the learning process and generative nature of the results remained unanswered. I use information theory to define a new approach to interpret training and the converged solutions of our models. The variational and generative nature of Autoencoders provides a platform to develop general models. Their results should extrapolate and allow generalisation beyond the boundaries of the observed data. To this extent, we introduce for the first time a new interpretation of the embedded generative functions through Perturbation Theory. The embedding multiplicity is addressed by transforming the distributions into a new set of generalisable functions, while characterising their energy spectrum under a particular energy landscape. We outline the combination of theoretical and machine learning based methods, for moving towards interpretable and generalisable models. Developing a theoretical framework to map from genotype to phenotype, we provide both supervised and unsupervised tools to operate over single cell RNA-Seq. data. We have generated a pipeline to identify relevant genes and cell types through Variational Autoencoders (VAEs), validating reconstructed gene expressions to prove the generative performance of the embeddings. The new interpretation of the information learned and extracted by the models de fines a label independent evaluation, particularly useful for unsupervised learning. Lastly, we introduce a novel transformation of the generative embeddings based on quantum and perturbation theory. Our contributions can and have been extended to new datasets, according to the nature of the tasks being explored. For instance, the combination of unsupervised learning and information theory can be applied to a variety of biological or medical data. We have trained several VAE models with additional cancer and metabolic data, proving to extract meaningful representations of the data. The perturbation theory transformation of the embedding can also lead to future research on the generative potential of Variational Autoencoders through a physics perspective, combining statistical and quantum mechanics. We believe that machine learning will only continue its fast expansion and growth through the development of more generalisable more interpretable models. "Prediction is very difficult, especially if it's about the future" Niels Boh

Problemas de salud, de trabajadores de enfermería en ambulatorios, causados por exposición a cargas fisiológicas

Objetivo: Identificar os problemas de saúde gerados na exposição a cargas fisiológicas, o Índice de Massa Corporal (IMC) dos trabalhadores de enfermagem e o impacto destes na qualidade de vida dos trabalhadores de enfermagem. Métodos: Estudo descritivo de abordagem quanti-qualitativa realizado em um ambulatório de um hospital universitário da cidade de São Paulo. A população foi constituída pelos 19 trabalhadores de enfermagem da unidade. O instrumento de coleta de dados foi o formulário, aplicado pela técnica de entrevista. Resultados: A alta freqüência de exposição dos trabalhadores a cargas fisiológicas (42,1%), associada ao alto IMC (26,3% obesos) é geradora de distúrbios osteomusculares relacionados à prática do trabalho, cujo principal sintoma é a dor em diferentes regiões. Também as varizes e os microvasos nas pernas e calosidades nos pés são relatados. Conclusão: Apesar do ambulatório ser considerado "prêmio", os problemas de saúde relatados interferem negativamente na qualidade de vida e trabalho.Objective: To identify health problems among outpatient nursing personnel with high physiological workload and high Body Mass Index (BMI]), and their impact on the nursing personnel quality of life. Methods: A descriptive quantitative-qualitative approach was used. The sample consisted of 22 nursing personnel from an outpatient clinic from a university teaching hospital of São Paulo city. Data was collected though interview using a specific instrument developed for the study. Results: Nursing personnel physiological workload (42.11%) and high BMI (26.32%) were associated with musculoskeletal disorders characterized by pain in different parts of the body, leg varices and microvessels, and foot callosities. Conclusion: Although working at the outpatient clinic is perceived as "a gift," the reported health problems negatively affected the quality of life and the job performance of the nursing personnel.Objetivo: Identificar los problemas de salud, generados por la exposición a cargas fisiológicas, el Índice de Masa Corporal (IMC), de los trabajadores de enfermería y el impacto de este en la calidad de vida de esos trabajadores. Métodos: Estudio descriptivo de abordaje cuantativo y cualitativo realizado en el ambulatorio de un hospital universitario de la ciudad de Sao Paulo. La población fue constituida por los 22 trabajadores de enfermería de la unidad. El instrumento de recolección de datos fue el formulario, aplicado por la técnica que realizó la entrevista. Resultados: La alta frecuencia de exposición de los trabajadores a cargas fisiológicas (42,11%), asociada al alto IMC (26,32% obesos) genera disturbios osteomusculares relacionados a la práctica del trabajo, cuyo principal síntoma es el dolor en diferentes regiones. También, fueron relatados varices y pequeños vasos sanguíneos en las piernas y callosidades en los pies. Conclusión: A pesar de que el ambulatorio es considerado un "premio", los problemas de salud relatados interfieren negativamente en la calidad de vida y trabajo

Internalisation of cell-penetrating peptides into tobacco protoplasts

AbstractCells are protected from the surrounding environment by plasma membrane which is impenetrable for most hydrophilic molecules. In the last 10 years cell-penetrating peptides (CPPs) have been discovered and developed. CPPs enter mammalian cells and carry cargo molecules over the plasma membrane with a molecular weight several times their own. Known transformation methods for plant cells have relatively low efficiency and require improvement. The possibility to use CPPs as potential delivery vectors for internalisation in plant cells has been studied in the present work. We analyse and compare the uptake of the fluorescein-labeled CPPs, transportan, TP10, penetratin and pVEC in Bowes human melanoma cells and Nicotiana tabacum cultivar (cv.) SR-1 protoplasts (plant cells without cell wall). We study the internalisation efficiency of CPPs with fluorescence microscopy, spectrofluorometry and fluorescence-activated cell sorter (FACS). All methods indicate, for the first time, that these CPPs can internalise into N. tabacum cv. SR-1 protoplasts. Transportan has the highest uptake efficacy among the studied peptides, both in mammalian cells and plant protoplast. The internalisation of CPPs by plant protoplasts may open up a new effective method for transfection in plants

Editorial : Can population health be personalized? Estonia and Finland as examples

Non peer reviewe