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I presocratici nelle Enneadi
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Mobile Application Security in the Presence of Dynamic Code Updates
No full textThe increasing number of repeated malware penetrations into official mobile app markets poses a high security threat to the confidentiality and privacy of end users' personal and sensitive information. Protecting end user devices from falling victims to adversarial apps presents a technical and research challenge for security researchers/engineers in academia and industry. Despite the security practices and analysis checks deployed at app markets, malware sneak through the defenses and infect user devices. The evolution of malware has seen it become sophisticated and dynamically changing software usually disguised as legitimate apps. Use of highly advanced evasive techniques, such as encrypted code, obfuscation and dynamic code updates, etc., are common practices found in novel malware. With evasive usage of dynamic code updates, a malware pretending as benign app bypasses analysis checks and reveals its malicious functionality only when installed on a user's device.
This dissertation provides a thorough study on the use and the usage manner of dynamic code updates in Android apps. Moreover, we propose a hybrid analysis approach, StaDART, that interleaves static and dynamic analysis to cover the inherent shortcomings of static analysis techniques to analyze apps in the presence of dynamic code updates. Our evaluation results on real world apps demonstrate the effectiveness of StaDART. However, typically dynamic analysis, and hybrid analysis too for that matter, brings the problem of stimulating the app's behavior which is a non-trivial challenge for automated analysis tools.
To this end, we propose a backward slicing based targeted inter component code paths execution technique, TeICC. TeICC leverages a backward slicing mechanism to extract code paths starting from a target point in the app. It makes use of a system dependency graph to extract code paths that involve inter component communication. The extracted code paths are then instrumented and executed inside the app context to capture sensitive dynamic behavior, resolve dynamic code updates and obfuscation. Our evaluation of TeICC shows that it can be effectively used for targeted execution of inter component code paths in obfuscated Android apps. Also, still not ruling out the possibility of adversaries reaching the user devices, we propose an on-phone API hooking based app introspection mechanism, AppIntrospector, that can be used to analyze, detect and prevent runtime exploitation of app vulnerabilities that involve dynamic code updates
A framework for integrating user-centred Design and agile Development in small Companies
Get PDFThe integration of user-centred design (UCD) and Agile development is gaining increasing momentum in industry: the two approaches show promising complementarities and their convergence can lead to a more holistic software engineering approach relative to the application of just one of them. However, the practicalities of this integration are not trivial and the topic is currently of interest to a variety of research communities. This thesis aims at understanding the integration of user-centred design and Agile development and at supporting its adoption in small companies. Based on a qualitative approach, it is positioned at the intersection of Computer Supported Cooperative Work and software engineering, and is grounded on several empirical studies performed in industry. The work is organised in three stages inspired by the action research approach. The first stage was dedicated to understanding software development practice: through literature review and two ethnographically-informed studies, it resulted in the first contribution of this thesis, that is a set of communication breakdowns that may hinder the integration of user-centred design and Agile development.
The second stage was dedicated to deliberating improvements of practice: the set of communication breakdowns was elaborated into the second contribution of this thesis, that is a framework of focal points meant to help the organisation diagnose and assess communication breakdowns in its work practice.
The third stage of the thesis was dedicated to implementing and evaluating improvements. The framework was further elaborated into a training on the adoption of the framework itself. Such training was instantiated in two iterations of action research performed in small development organisations, with the aim of establishing a supportive organisational environment and mitigating communication breakdowns. Once validated through these cases, the training constituted the third contribution of this thesis. Results show that the intervention has benefited companies at several levels, enriching work practice with fresh techniques, favouring team collaboration and cooperation, and resulting in a shift from a technology-centred mindset to a more user-centred one
Bridging Sensor Data Streams and Human Knowledge
Get PDFGenerating useful knowledge out of personal big data in form of sensor streams is a difficult task that presents multiple challenges due to the intrinsic characteristics of these type of data, namely their volume, velocity, variety and noisiness. This problem is a well-known long standing problem in computer science called the Semantic Gap Problem. It was originally defined in the research area of image processing as "... the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation..." [Smeulders et al., 2000]. In the context of this work, the lack of coincidence is between low-level raw streaming sensor data collected by sensors in a machine-readable format and higher-level semantic knowledge that can be generated from these data and that only humans can understand thanks to their intelligence, habits and routines.
This thesis addresses the semantic gap problem in the context above, proposing an interdisciplinary approach able to generate human level knowledge from streaming sensor data in open domains. It leverages on two different research fields: one regarding the collection, management and analysis of big data and the field of semantic computing, focused on ontologies, which respectively map to the two elements of the semantic gap mentioned above.
The contributions of this thesis are:
• The definition of a methodology based on the idea that the user and the world surrounding him can be modeled, defining most of the elements of her context as entities (locations, people, objects, among other, and the relations among them) in addition with the attributes for all of them. The modeling aspects of this ontology are outside of the scope of this work. Having such a structure, the task of bridging the semantic gap is divided in many, less complex, modular and compositional micro-tasks that are which consist in mapping the streaming sensor data using contextual information to the attribute values of the corresponding entities. In this way we can create a structure out of the unstructured, noisy and highly variable sensor data that can then be used by the machine to provide personalized, context-aware services to the final user;
• The definition of a reference architecture that applies the methodology above and addresses the semantic gap problem in streaming sensor data;
• The instantiation of the architecture above in the Stream Base System (SB), resulting in the implementation of its main components using state-of-the-art software solutions and technologies;
• The adoption of the Stream Base System in four use cases that have very different objectives one respect to the other, proving that it works in open domains
Host lncRNAs recognize invading bacteria and aid against infection by associating with polysomes
Get PDFBACKGROUND: Infections mediated by pathogens, such as bacteria, often interfere with the host process of protein synthesis. This process is the most energy consuming in cells, that is why cells fine-tune it to conserve energy and respond quickly to stress. Thus, translation should be tightly regulated upon bacterial infection, yet the picture is still sketchy at best. Surprisingly, lncRNAs have recently been found to associate with ribosomes and polysomes; however, to date no research exists that addresses their role in translation regulation upon bacterial infection.
AIM: The key question I wanted to answer during my PhD is whether host produced lncRNAs rewire the cell’s translation upon infection, inducing pathogen-specific and virulent factor-specific translational controls to cope with the infection.
EXPERIMENTAL APPROACHES: To address the abovementioned aim, I used human colon epithelial cells (Caco-2) and Listeria monocytogenes as a host-pathogen model to explore the host cell’s response to infection at the translational level. By using a WT and a strain deficient for the expression of the main virulent factor Listeriolysin O (LLO-deficient (∆LLO) strain). Taking advantage of these strains, I explored whether the pore forming toxin, LLO, is able to trigger a host toxin-specific translational controls. To address this question, I massively employed polysome profiling, a classical approach to study translation and Next Generation Sequencing (NGS) to monitored changes in the transcriptome and the translatome upon infection at early time-points after infection and studying the possible function and mechanism of two lncRNAs that I found to be over-expressed upon infection.
RESULTS: I showed that infection with either bacterial strain induced strong translational defects especially upon infection with WT Listeria. Cells responded to the infection by expressing numerous lncRNA and uploading them on polysomes. By comparing the transcriptome and the translatome of cells infected with either WT and ∆LLO I focused on two lincRNAs. The first, AC016831.1, is strongly upregulated upon infection with both strains of Listeria and exclusively associates with small active polysomes. In fact, my results show strong evidence that AC016831.1 is in fact actively associated with translating ribosomes and bioinformatics analyses of co-expressed genes showed its involvement in the innate immune response. The second, MIR181A1HG displayed a Listeria-specific and LLO-specific upregulation upon infection. I demonstrated that it is strongly associated with inactive stalled small polysomes. Importantly, its expression upon infection exerted a protective role against bacterial replication in host cells. Considering the obtained results, I propose that MIR181A1HG is acting as a ribosome sponge, decreasing the number of available ribosomes, ultimately leading to translation down-regulation. This role may help cells to keep the overall protein production rate at a low pace during infection, allowing the host to properly activate the innate immune system and fight-off the pathogen.
In this research, using polysome profiling, I demonstrated for the first time that lncRNAs play a role in the host-pathogen crosstalk by rewiring translation. Evidence shows they might be even producing peptides, challenging their non-coding status and paving the way for understanding the possible role of short peptides in controlling bacterial infections
Understanding the Organization and functional Control of Polysomes by integrative Approaches
Get PDFBackground and rationale
Translation is a fundamental biological process occurring in cells, carried out by ribosomes simultaneously bound to an mRNA molecule (polyribosomes). It has been exhaustively demonstrated that dysregulation of translation is implicated in a wide collection of pathologies including tumours and neurological disorders. Latest findings reveal the existence of translational regulatory mechanisms acting in cis or trans with respect to the mRNAs and governing the movement and the position of ribosomes along transcripts or directly impacting on the ribosome catalogue of its constituent proteins. For this reason, translational controls also account for widespread uncoupling between transcript and protein abundances in cells.
To explain the poor correlation between transcripts and protein levels, many computational models of translation have been developed. Usually, these approaches aim at predicting protein abundances in cells starting from the mRNA abundance. Despite the efforts of these modelling studies, a consensus model remains elusive, drawing to contradictory conclusions concerning the role of mRNA regulatory elements such as the usage of codons (codon usage bias) and slowdown mechanism at the beginning of the coding sequence (ramp). More recently, following the rapid and widespread diffusion of ribosome footprinting assays (RiboSeq), which enables the dissection of translation at single nucleotide resolution, a number of computational pipelines dedicated to the analysis of RiboSeq data have been proposed. These tools are typically designed for extracting gene expression alterations at the translational level, while the positional information describing fluxes and positions of ribosomes along the transcript is still underutilized.
Therefore, the polysome organization, in term of number and position of ribosomes along the transcript and the translational controls directed in shaping cellular phenotypes is still open to breakthrough discoveries.
Broad objectives
The aim of my thesis is the development of mathematical and computational tools integrated with experimental data for a comprehensive understanding of translation regulation and polysome organization rules governing the number of ribosomes per polysome and the ribosome position along transcripts.
Project design and methods
With this purpose, I developed riboWaves, an integrated bioinformatics suite divided in two branches. riboWaves includes in the first branch two modeling modules: riboAbacus, predicting the number of ribosomes per transcript, and riboSim, predicting ribosome localization along mRNAs. In the second branch, riboWaves provides two pipelines, riboWaltz and riboScan, for detailed analyses of ribosome profiling data aimed at providing meaningful and yet unexplored ribosome positional information. The models and the pipelines are implemented in C and R, respectively. riboAbacus and riboWaltz are available on GitHub.
Results
To predict the number of ribosomes per transcript and the position of ribosomes on mRNAs, I applied riboAbacus and riboSim, respectively, to transcriptomes of different organisms (yeast, mouse, human) for understanding the role of translational regulatory elements in tuning polysome in different organisms. First, I trained and validated performances of riboAbacus taking advantage of Atomic Force Microscopy images of polysomes, while performances of riboSim were assessed employing ribosome profiling data. Predictions provided by riboAbacus and riboSim were evaluated in parallel. I showed that the average number of ribosomes translating a molecule of mRNA can be well explained by the deterministic model, riboAbacus, that includes as features the mRNA levels, the mRNA sequences, the codon usage bias and a slowdown mechanism at the beginning of the CDS (ramp hypothesis). The predictions of ribosome localization by riboSim that used as features the mRNA sequence, the codon usage and the ramp, were run for yeast, mouse and human. I observed a good similarity between the predicted and experimental positions of ribosomes along transcripts in yeast, while poor similarity was obtained between predicted and experimental ribosome positions in the two mammals, suggesting the presence of more elaborate controls that tune ribosomes movement in higher eukaryotes than in simple species.
After having developed two tools for the analyses of RiboSeq data and extraction of positional information on ribosome localization along transcripts, I applied both riboWaltz and riboScan in a case study. The aim was to dissect possible defects in ribosome localization in tissues of a mouse model of Spinal Muscular Atrophy (SMA). SMA is a neurodegenerative disorder caused by low levels of the Survival of Motor Neuron protein (SMN) in which translational impairments are recently emerging as possible cause of the disease. I analysed ribosome profiling data obtained from three different types of RiboSeq variants in healthy and SMA-affected mouse brains at the early-symptomatic stage of the disease. I observed i) a significant drop-off of translating ribosomes along the coding sequence in the SMA condition (using riboWaltz); ii) in SMA-affected mice, the possible accumulation of ribosomes along the 3' UTR in neuro-related mRNAs (using riboScan); iii) the involvement of SMN-specialized ribosomes in playing a very intimate role with the elongation stage of translation of the first codons of transcripts (riboWaltz), iv) the loss of ribosomes at the 3rd codon in SMA in transcripts bound by SMN-specialized ribosomes and v) a remarkable connection between SMN and the down-regulation of genes in SMA-affected mice. Overall, these findings confirmed previous observation about possible SMN-related dysregulations of local protein synthesis in neurons. More importantly, they unravel a completely new role of SMN in tuning translation at multiple levels (initiation, elongation and the recycling of terminating ribosomes), opening new hypotheses and scenarios for explaining the most devastating genetic disease, leading cause worldwide of infant mortality.
Conclusions
The present work provides a new comprehensive and integrated scenario for better understanding translation and demonstrates that this approach is a very powerful strategy to pave the way for new understanding of fine alteration in polysome organization and functional control in both physiological and pathological conditions
Events based Multimedia Indexing and Retrieval
Get PDFEvent recognition is one of multimedia applications that has been gaining ground recently. However, it has received scarce attention relatively to other applications. The methodologies presented hereby are aimed at event-based analysis of multimedia content, which is achieved from three perspectives, namely (i) event recognition in single images, (ii) event recognition in personal photo collections and (iii) fusion of social media information and satellite imagery for natural disaster detection. A close look at the relevant literature suggests that more attention has been paid to event recognition in single images. Event recognition in personal photo collection has also received a number of interesting solutions. Natural disaster detection in images from social media and satellite imagery, however, is relatively new. As a matter of fact, many issues remain unsolved mostly due to the heterogeneity, multi-modality and the unstructured nature of the data. In this dissertation, such open problems are presented and analyzed. New perspectives and approaches are suggested, alongside a detailed experimental validation and analysis. In details, our contribution is multi-fold. On the one hand, we aim at demonstrating that the fusion of different feature extraction and classification strategies can outperform the single methods by jointly exploiting the learning capabilities of individual deep models. On the other side, we analyze the importance of event-salient objects and local image regions in event recognition. We also present a novel framework for event recognition in personal photo collections. Moreover, we also present our system JORD, and our Convolutional Neural Networks (CNNs) and Generative Adversarial Network (GAN) based fusion of social media and satellite images for natural disaster detection. A thorough experimental analysis of each proposed solution is provided on benchmark datasets along with the potential direction of future work
Molecular communication between artificial cells
Get PDFGenetic engineering has been widely used to reprogram cells for a variety of purposes, suggesting a wide range of possible applications in industrial and academic
research. Although the techniques available are very well established, the mechanisms of cellular life are not completely understood. Therefore, despite offering a versatile tool, engineered cells are prone to possible unexpected behaviors. Investigations of more controllable systems are partially focused on the creation of cellular mimics assembled from discrete components with defined properties. The controlled assembly of molecules allows the creation of entities able to form compartments in water solutions and to carry out enzymatic reactions or gene expression. These artificial cells are able to establish communication pathways with natural cells and may be further developed to fight pathogens or cancer cells, for
example. Despite these promising results, technological applications based on cellular mimics necessitate further technical improvements. A considerable defect of artificial cells is the lack of some mechanisms for selfsustainment that are instead present in engineered living cells. Besides few strategies aimed at energy restoration, artificial cells are not yet able to efficiently use the available resources in their environment. Considering these technical limitations, this thesis proposes to improve communication pathways between artificial and natural cells by exploiting multiple kinds of cellular mimics. Artificial cells can vary in composition and if engineered to coordinate activity, could be capable of overcoming individual weaknesses.
To investigate the possibility of creating communities of artificial cells that collaborate with each other, the work described here was focused on establishing molecular communication pathways between two kinds of artificial cells. The designed communication was based on the exchange of chemical messages between two cellular
mimics resulting either in genetic regulation or enzymatic reactions. On one side, lipid vesicles carrying gene expression through in vitro transcription and translation reactions and on the other side a novel structure composed of modified proteins, named proteinosome, to carry out enzymatic reactions. Each part of the communication pathway was separately investigated. Some efforts were put into the characterization of genetic switches so as to be able to better tune gene expression. All the other components were then singularly tested before combining together.
One way that artificial cells, either alone or in a community, can function as a useful technology is if the artificial cells are able to sense and respond to environmental changes. The sensing functionality can be conferred by natural or synthetic transcriptional regulators. It is possible to modify biological macromolecules to interact with chemical messages released by natural cells. The second part of the thesis summarizes two distinct works aimed at developing two kinds of biosensors with potential applications within artificial cells. Several technical problems arose while testing the communication pathway, and it was necessary to change the initial strategy to include engineered cells. Nonetheless, the work presented here offers a method for the establishment of molecular communication pathways within communities of artificial cells that could serve as the basis for future implementation in more efficient communication systems
Studies of Cortical Plasticity in the Normal and the Diseased Brain
No full textDespite the large amount of work that has been conducted since Donald’s Hebb work and described in his famous dissertation “The Organization of Behavior” (Hebb, 1949), understanding the experience-dependent mechanisms of plasticity within the primary visual cortex (V1) remains a major priority. Although plasticity effects are strongest during the critical period (early development), studies on cortical plasticity from the last two decades have clearly demonstrated that the human brain is plastic and amenable to changes throughout life. Perceptual Learning (PL) is one of the most commonly used procedure to promote visual improvement in neurotypicals and recovery of functions in a variety of disorders. However, a common feature to most of the training protocols is that they require long time and a high number of sessions to show effective improvement. Recently, non-invasive brain stimulation (NIBS) techniques, specifically transcranial random noise stimulation (tRNS) and anodal tDCS, have been used to modulate activity within the visual cortex to enhance perceptual learning. However, the mechanisms of action and the long-term effects on learning are still unknown. The questions this thesis work will address are the following: (1) can neuromodulatory techniques be used to boost visual perceptual learning in neurotypicals, which technique is the most effective and what are the long term effects on learning? (2) what are the potential underlying physiological mechanisms modulating cortical excitability of the visual cortex? and (3) contingent upon results from (1), can NIBS be used over early, peri-lesional visual areas during visual training to induce recovery of visuo-perceptual abilities in chronic partial cortical blindness (CB)? I used tRNS coupled with visuo-perceptual training protocols to promote fast and sustained perceptual learning in neurotypicals. I then provide evidence that tRNS can increase cortical excitability of the visual cortex, measured by priming early visual areas with tRNS, before measuring phosphene threshold with single pulse TMS. Lastly, I provide preliminary evidence of the effectiveness of tRNS in promoting recovery of visual field deficits in partial cortical blindness
Where symbols meet meanings: The organization of gestures and words in the middle temporal gyrus
No full textEvery day we use actions, gestures and words to interact with other people and with the environment. Being able to understand people’s movements and communicative intentions is critical to our ability to act successfully in the world. Here we present three studies aiming at investigating the relationship between actions, gestures and words in the brain. In the first study we described and offer a standardized data set of 230 well-controlled stimuli of meaningful (pantomimes and emblems) and meaningless gestures together with norms, with the aim of promoting replicability between studies. One hundred and thirty raters (Italian and non-Italian speakers) rated the meaningfulness of the gestures, and provided a name and a description for each of them. To our knowledge, this is the first data set of meaningful and meaningless gestures presented in the literature. In the second study, we aimed 1) at characterizing the neural network associated with the processing of different categories of gestures (pantomimes, emblems and meaningless gestures) using fMRI, and 2) at contrasting the role of precentral and temporal areas in action understanding, using rTMS. In particular, we applied rTMS to the posterior middle temporal gyrus (pMTG) and to the ventral premotor cortex (PMv) in different sessions, while participants were performing either a semantic or a perceptual judgment task. According to motor theories of action understanding, rTMS applied to the PMv, but not to the pMTG, should impair performance during the semantic judgment task. By contrast, according to cognitive theories of action understanding, rTMS applied to pMTG, but not to PMv, should impair performance in this task. Results from the fMRI experiment revealed a sensitivity of the MTG to meaningful in comparison to meaningless gestures. Additionally, three different brain areas seemed to contribute to the processing of pantomimes and emblems: superior parietal lobe (SPL) and precentral gyrus (PCG) in the case of pantomimes and IFG in the case of emblems. Unfortunately, we did not observe any significant effect of rTMS in any condition. The third study aimed at investigating how pantomimes, emblems and words are organized in the middle temporal gyrus, using fMRI. We observed a posterior-to-anterior structure, both in the left and in the right hemisphere, that might reflect the input modality and also the arbitrariness of the relationship between form and meaning