Sequential action of Rab proteins along the endocytic-recycling pathway

The endocytic pathway in eukaryotic cells is organized into distinct compartments. Early endosomes are heterogeneous and dynamic organelles, which are found both spread through the cortical cytoplasm (sorting-early endosomes) and in the perinuclear region (recycling endosomes). Endocytosed molecules that enter sorting endosomes can be directed to the degradative pathway or recycled back to the plasma membrane, either directly or by passing through perinuclear-recycling endosomes. Very little is known about the molecular mechanisms that regulate transport between these different compartments and the functional properties of endocytic organelles. In this thesis, I have investigated the molecular regulation of the endocytic-recycling pathway and, in particular, how endocytosis and recycling are co-ordinated at the level of early endosomes. To this end, I have addressed the function of Rab5, Rab4, and Rabll, three small GTPases that control trafficking from the plasma membrane to the early endosomes, sorting of cargo within early endosomes and recycling to the plasma membrane, respectively. Rab proteins function in their GTP-bound active state through the recruitment of effector proteins to the membrane compartment where they are localized. The identification of a large number of Rab5 effectors, together with the realization that these proteins act co-operatively, led to the proposal that this GTPase organizes the endosomal membrane into a biochemically and functionally distinct membrane domain. If generalized to other family members, this model would predict that other Rab proteins present on endosomes such as Rab4 and Rabll should be localized to discrete, non-overlapping membrane domains distinct from the one occupied by Rab5. Moreover, the finding that one Rab5 effector, Rahaptin-5, interacts also with Rah4 raises the possibility that the activity of these two GTPases is molecularly co-ordinated. Given the complementary role of Rab5 and Rab4 in regulating entry in, and exit out, of early endosomes the coupling of their activity through a common effector may ensure co-ordination of the endocytic and recycling functions of early endosomes. I have experimentally tested the working hypothesis predicting that divalent Rab effectors may regulate the association between contiguous Rab domains to allow the sequential transport of cargo along the endocytic-recycling pathway. To provide evidence for this model, two complementary approaches have been undertaken. First, I have developed an in vitro recycling assay to identify and characterize molecules involved in this process. Second, I have conducted a high-resolution morphological analysis of the endocytic recycling pathway in stable cell lines expressing GFP-tagged versions of Rab proteins. The results obtained support the idea that endosomes are indeed organized into distinct domains, harbouring Rab5, Rab4 and Rabll. The association between these domains follows a non-random distribution giving rise to three major populations of endosomes: one containing Rab5, a second with Rab4 and Rab5, and a third containing Rab4 and Rabll. Upon endocytosis, recycling cargo first enters Rab5 domains, and then progresses through Rab5/Rab4 and Rab4/Rabll endosomes before returning to the plasma membrane. Based on these observations, I then addressed the question of how the communication between neighbouring domains is regulated by specifically searching for Rab5 and Rab4 common effectors using an affinity chromatography approach. The results of these experiments led me to identify Rahenosyn-5 as a novel Rab5 and Rab4 common effector. Subsequently, I demonstrated that Rabenosyn-5, as well as Rabaptin-5 over-expression specifically increased the association between Rab5 and Rab4 endosomal domains and stimulated transferrin recycling. Concomitantly, the fraction of Rah4+Rabll positive structures was reduced and transport to peri-nuclear recycling endosomes decreased. Thus, divalent Rab5 and Rab4 effectors regulate endocytosis and recycling by connecting Rab5 and Rab4 domains on early endosomes. These results provide support for the hypothesis that Rab proteins and their effectors regulate the compartmental organization and trafficking function of early endosomes

Exploiting Large Language Models to Train Automatic Detectors of Sensitive Data

openThis thesis proposes an automated system designed to identify sensitive data within text documents, aligning with the definitions and regulations outlined in the General Data Protection Regulation (GDPR). It reviews the current state of the art in Personally Identifiable Information (PII) and sensitive data detection, and how machine learning models for Natural Language Processing (NLP) are tailored to perform these tasks. A critical challenge addressed in this work pertains to the acquisition of suitable datasets for the training and evaluation of the proposed system. To overcome this obstacle, we explore the use of Large Language Model (LLM)s to generate synthetic datasets, thus serving as a valuable resource for training classification models. Both proprietary and open-source LLMs are leveraged to investigate the capabilities of local models in document generation. It then presents a comprehensive framework for sensitive data detection, covering six key domains and proposing specific criteria to identify the disclosure of sensitive data, which take into account the context and the domain relevance. To achieve the detection of sensitive data, a variety of models are explored, mainly based on the Transformer architecture (Bidirectional Encoder Representations from Transformers (BERT)), adapted to fulfill tasks of text classification and Named Entity Recognition (NER). It evaluates the performance of the models using fine-grained metrics, and shows that the NER model achieves the best results (90% score) when trained interchangeably on both datasets, also confirming the quality of the dataset generated with the open source LLM.This thesis proposes an automated system designed to identify sensitive data within text documents, aligning with the definitions and regulations outlined in the General Data Protection Regulation (GDPR). It reviews the current state of the art in Personally Identifiable Information (PII) and sensitive data detection, and how machine learning models for Natural Language Processing (NLP) are tailored to perform these tasks. A critical challenge addressed in this work pertains to the acquisition of suitable datasets for the training and evaluation of the proposed system. To overcome this obstacle, we explore the use of Large Language Model (LLM)s to generate synthetic datasets, thus serving as a valuable resource for training classification models. Both proprietary and open-source LLMs are leveraged to investigate the capabilities of local models in document generation. It then presents a comprehensive framework for sensitive data detection, covering six key domains and proposing specific criteria to identify the disclosure of sensitive data, which take into account the context and the domain relevance. To achieve the detection of sensitive data, a variety of models are explored, mainly based on the Transformer architecture (Bidirectional Encoder Representations from Transformers (BERT)), adapted to fulfill tasks of text classification and Named Entity Recognition (NER). It evaluates the performance of the models using fine-grained metrics, and shows that the NER model achieves the best results (90% score) when trained interchangeably on both datasets, also confirming the quality of the dataset generated with the open source LLM

Retail payments and the real economy

This paper examines the fundamental relationship between retail payments and the real economy. Using data from across 27 European markets over the period 1995-2009, the results confirm that migration to efficient electronic retail payments stimulates the overall economy, consumption and trade. Among different payment instruments, this relationship is strongest for card payments, followed by credit transfers. Cheque payments are found to have a relatively low macroeconomic impact. Retail payment transaction technology itself is also associated positively to real economic aggregates. We also show that initiatives to integrate and harmonise retail payment markets foster trade and consumption and thereby have a beneficial effect for whole economy. Additionally, the findings reveal that the impact of retail payments on the real economy is more pronounced in euro area countries. Our findings are robust to different regression specifications. The study supports the adoption of policies promoting a swift migration to efficient and harmonised electronic payment instruments

An Optogenetic Method to Modulate Cell Contractility during Tissue Morphogenesis

SummaryMorphogenesis of multicellular organisms is driven by localized cell shape changes. How, and to what extent, changes in behavior in single cells or groups of cells influence neighboring cells and large-scale tissue remodeling remains an open question. Indeed, our understanding of multicellular dynamics is limited by the lack of methods allowing the modulation of cell behavior with high spatiotemporal precision. Here, we developed an optogenetic approach to achieve local modulation of cell contractility and used it to control morphogenetic movements during Drosophila embryogenesis. We show that local inhibition of apical constriction is sufficient to cause a global arrest of mesoderm invagination. By varying the spatial pattern of inhibition during invagination, we further demonstrate that coordinated contractile behavior responds to local tissue geometrical constraints. Together, these results show the efficacy of this optogenetic approach to dissect the interplay between cell-cell interaction, force transmission, and tissue geometry during complex morphogenetic processes

Assessing Web Services Interfaces with Lightweight Semantic Basis

In the last years, Web Services have become the technological choice to materialize the Service-Oriented Computing paradigm. However, a broad use of Web Services requires efficient approaches to allow service consumption from within applications. Currently, developers are compelled to search for suitable services mainly by manually exploring Web catalogs, which usually show poorly relevant information, than to provide the adequate "glue-code" for their assembly. This implies a large effort into discovering, selecting and adapting services. To overcome these challenges, this paper presents a novel Web Service Selection Method. We have defined an Interface Compatibility procedure to assess structural-semantic aspects from functional specifications - in the form of WSDL documents - of candidate Web Services. Two different semantic basis have been used to define and implement the approach: WordNet, a widely known lexical dictionary of the English language; and DISCO, a database which indexes co-occurrences of terms in very large text collections. We performed a set of experiments to evaluate the approach regarding the underlying semantic basis and against third-party approaches with a data-set of real-life Web Services. Promising results have been obtained in terms of well-known metrics of the Information Retrieval field

A Software Tool for Selection and Integrability on Service Oriented Applications

Connecting services to rapidly developing service-oriented applications is a challenging issue. Selection of adequate services implies to face an overwhelming assessment effort, even with a reduced set of candidate services. On previous work we have presented an approach for service selection addressing the assessment of WSDL interfaces and the expected execution behavior of candidate services. In this paper we present a plugin for the Eclipse IDE to support the approach and to assist developers’ daily tasks on exploring services integrability. Particularly for behavioral compatibility we make use of two testing frameworks: JUnit and MuClipse to achieve a compliance testing strategy.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Parameter estimation of binary black holes in the endpoint of the up-down instability

Black-hole binary spin precession admits equilibrium solutions corresponding to systems with (anti-) aligned spins. Among these, binaries in the up-down configuration, where the spin of the heavier (lighter) black hole is co- (counter-) aligned with the orbital angular momentum, might be unstable to small perturbations of the spin directions. The occurrence of the up-down instability leads to gravitational-wave sources that formed with aligned spins but are detected with precessing spins. We present a Bayesian procedure based on the Savage-Dickey density ratio to test the up-down origin of gravitational-wave events. This is applied to both simulated signals, which indicate that achieving strong evidence is within the reach of current experiments, and the LIGO/Virgo events released to date, which indicate that current data are not informative enough