Bacterial protein sorting: experimental and computational approaches

All living cells are subdivided into different compartments that are separated by membranes, which are essentially impermeable to water-soluble molecules. Since proteins are predominantly synthesized in the cytoplasm, specific sorting mechanisms and signals known as signal peptides are required to target them to other cellular compartments or the extracellular milieu. The research presented in this PhD thesis was focused on principles of protein sorting and secretion in bacteria, which were investigated with novel approaches that combined experimental analyses and computational tools. In particular, the studies addressed the bacterial cell factory Bacillus subtilis and the pathogens Porphyromonas gingivalis and Staphylococcus aureus. The results show how computational approaches can greatly enhance the experimental studies. In particular, this concerned predictions of subcellular protein localization with tailored tools that were developed for the different bacteria. These tools can enhance industrial and domestic applications of proteins produced with bacteria, or foster the identification of novel drugs or drug targets. In addition, the relationship between secretion efficiency and different features of signal peptides was investigated using a designed signal peptide library and an innovative high-throughput assay. The outcomes were used to generate a machine learning model that predicts signal peptide efficiency in directing protein secretion, and explains the relevant physico-chemical features of signal peptides. Importantly, the model allows de novo design of signal peptides that can be exploited in high-performing protein secretion systems. Altogether, the studies highlight the advantages of combined computational-experimental approaches and how they are best exploited in future biotechnological, pharmaceutical and biomedical applications

Modelling sustainable human development in a capability perspective

In this paper we model sustainable human development as intended in Sen's capability approach in a system dynamic framework. Our purpose is to verify the variations over time of some achieved functionings, due to structural dynamics and to variations of the institutional setting and instrumental freedoms (IF Vortex). The model is composed of two sections. The 'Left Side' one points out the 'demand' for functionings in an ideal world situation. The real world one, on the 'Right Side' indicates the 'supply' of functionings that the socio-economic system is able to provide individuals with. The general model, specifically tailored for Italy, can be simulated over desired time horizons: for each time period, we carry out a comparison between ideal world and real world functionings. On the basis of their distances, the model simulates some responses of decision makers. These responses, in turn influenced by institutions and instrumental freedoms, ultimately affect the dynamics of real world functionings, i.e. of sustainable human development.Functionings, Capabilities, Institutions, Instrumental Freedoms, Sustainable Human Development

Sensorless Pedalling Torque Estimation Based on Motor Load Torque Observation for Electrically Assisted Bicycles

The need for reducing the cost of and space in Electrically Assisted Bicycles (EABs) has led the research to the development of solutions able to sense the applied pedalling torque and to provide a suitable electrical assistance avoiding the installation of torque sensors. Among these approaches, this paper proposes a novel method for the estimation of the pedalling torque starting from an estimation of the motor load torque given by a Load Torque Observer (LTO) and evaluating the environmental disturbances that act on the vehicle longitudinal dynamics. Moreover, this work shows the robustness of this approach to rotor position estimation errors introduced when sensorless techniques are used to control the motor. Therefore, this method allows removing also position sensors leading to an additional cost and space reduction. After a mathematical description of the vehicle longitudinal dynamics, this work proposes a state observer capable of estimating the applied pedalling torque. The theory is validated by means of experimental results performed on a bicycle under different conditions and exploiting the Direct Flux Control (DFC) sensorless technique to obtain the rotor position information. Afterwards, the identification of the system parameters together with the tuning of the control system and of the LTO required for the validation of the proposed theory are thoroughly described. Finally, the capabilities of the state observer of estimating an applied pedalling torque and of recognizing the application of external disturbance torques to the motor is verified

Improvement of Position Estimation of PMSMs Using an Iterative Vector Decoupling Algorithm

This paper presents an improvement of sensorless techniques based on anisotropy for the estimation of the electrical angular position of synchronous machines by means of an iterative algorithm. The presented method reduces the effect of the fourth saliency harmonics on the measured signals avoiding the use of an observer or filter, thus, no additional dynamics are introduced on the system. Instead, a static algorithm based on iterative steps is proposed, minimizing the angular position error. The algorithm is presented and applied using the DFC (Direct Flux Control) technique but it is not limited to this choice. The advantages and limitations of this method are presented within this paper. The proof of the algorithm convergence is given. Simulations and experimental tests are performed in order to prove the effectiveness of the proposed algorithm

GP4:an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting

Subcellular localization is a critical aspect of protein function and the potential application of proteins either as drugs or drug targets, or in industrial and domestic applications. However, the experimental determination of protein localization is time consuming and expensive. Therefore, various localization predictors have been developed for particular groups of species. Intriguingly, despite their major representation amongst biotechnological cell factories and pathogens, a meta-predictor based on sorting signals and specific for Gram-positive bacteria was still lacking. Here we present GP(4), a protein subcellular localization meta-predictor mainly for Firmicutes, but also Actinobacteria, based on the combination of multiple tools, each specific for different sorting signals and compartments. Novelty elements include improved cell-wall protein prediction, including differentiation of the type of interaction, prediction of non-canonical secretion pathway target proteins, separate prediction of lipoproteins and better user experience in terms of parsability and interpretability of the results. GP(4) aims at mimicking protein sorting as it would happen in a bacterial cell. As GP(4) is not homology based, it has a broad applicability and does not depend on annotated databases with homologous proteins. Non-canonical usage may include little studied or novel species, synthetic and engineered organisms, and even re-use of the prediction data to develop custom prediction algorithms. Our benchmark analysis highlights the improved performance of GP(4) compared to other widely used subcellular protein localization predictors. A webserver running GP(4) is available at http://gp4.hpc.rug.nl

Dynamic Underwater Glider Network for Environmental Field Estimation

A coordinated dynamic sensor network of autonomous underwater gliders to estimate three-dimensional time-varying environmental fields is proposed and tested. Integration with a network of surface relay nodes and asynchronous consensus are used to distribute local information and achieve the global field estimate. Field spatial sparsity is considered, and field samples are acquired by compressive sensing devices. Tests on simulated and real data demonstrate the feasibility of the approach with relative error performance within 10

An abstract argumentation approach for the prediction of analysts’ recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis