Interplay of Exoribonucleases, Hfq and Small RNAs Structural Determinants in the Control of Gene Expression

Dissertation presented to obtain the Ph.D degree in Biology

Development of an affinity pair “Tag-Receptor” for recombinant protein expression and purification

Dissertação apresentada para a obtenção do Grau de Mestre em Biotecnologia, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Variation in protein expression levels in cell populations

Dissertation presented to obtain the Ph.D degree in Systems Biology

Regulation of gene expression by SnRK1 kinases and miRNAs during the plant stress response

Dissertation presented to obtain the Ph.D degree in Plant Physiology

Controlling gene expression in enterobacteriaceae: studies on sRNAs and strategies for synthetic biology

Dissertation presented to obtain the Master Degree in Molecular, Genetics and Biomedicine

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

Gold nanoprobes for assessing expression of critical genes in the infection pathway of MRSA

Staphylococcus aureus is an important opportunistic pathogen that can cause a wide variety of diseases from mild to life-threatening conditions. S. aureus can colonize many parts of the human body but the anterior nares are the primary ecological niche. Its clinical importance is due to its ability to resist almost all classes of antibiotics available together with its large number of virulence factores. MRSA (Methicillin-Resistant S. aureus) strains are particularly important in the hospital settings, being the major cause of nosocomial infections worldwide. MRSA resistance to β-lactam antibiotics involves the acquisition of the exogenous mecA gene, part of the SCCmec cassette. Fast and reliable diagnostic techniques are needed to reduce the mortality and morbidity associated with MRSA infections, through the early identification of MRSA strains. The current identification techniques are time-consuming as they usually involves culturing steps, taking up to five days to determine the antibiotic resistance profile. Several amplification-based techniques have been developed to accelerate the diagnosis. The aim of this project was to develop an even faster methodology that bypasses the DNA amplification step. Gold-nanoprobes were developed and used to detect the presence of mecA gene in S. aureus genome, associated with resistance traits, for colorimetric assays based on non-crosslinking method. Our results showed that the mecA and mecA_V2 gold-nanoprobes were sensitive enough to discriminate the presence of mecA gene in PCR products and genomic DNA (gDNA) samples for target concentrations of 10 ng/μL and 20 ng/μL, respectively. As our main objective was to avoid the amplification step, we concluded that the best strategy for the early identification of MRSA infection relies on colorimetric assays based on non-crosslinking method with gDNA samples that can be extracted directly from blood samples.