Modelling and Recognizing Personal Data

To define what a person is represents a hard task, due to the fact that personal data, i.e., data that refer or describe a person, have a very heterogeneous nature. The issue is only worsening with the advent of technologies that, while allowing unprecedented collection and processing capabilities, cannot \textit{understand} the world as humans do. 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...". In the context of this work, the semantic gap is the lack of coincidence is between sensor data collected by ubiquitous devices and the human knowledge about the world that relies on their intelligence, habits, and routines. This thesis addresses the semantic gap problem from a representational point of view, proposing an interdisciplinary approach able to model and recognize personal data in real life scenarios. In fact, the semantic gap affects many communities, ranging from ubiquitous computing to user modelling, that must face the issue of managing the complexity of personal data in terms of modelling and recognition. The contributions of this Ph. D. Thesis are: 1) The definition of a methodology based on an interdisciplinary approach that can account for how to represent and allow the recognition of personal data. The interdisciplinary approach relies on the entity-centric approach and on an interdisciplinary categorization to define and structure personal data. 2) The definition of an ontology of personal data to represent human in a general way while also accounting their different dimensions of their everyday life; 3) The instantiation of the personal data representation above in a reference architecture that allows implementing the ontology and that can exploit the methodology to account for how to recognize personal data. 4) The adoption of the methodology for defining personal data and its instantiation in three real-life use cases with different goals in mind, proving that our modelling works in different domains and can account for several dimensions of the user

Recognizing Hospital Care Activities with a Coat Pocket Worn Smartphone

In this work, we show how a smart-phone worn unobtrusively in a nurses coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor node worn in such an (from the sensing point of view) unfavorable location can still recognize complex, sometimes subtle activities. We evaluate our approach in a large real life dataset from day to day hospital operation. In total, 4 runs of patient care per day were collected for 14 days at a geriatric ward and annotated in high detail by following the performing nurses for the entire duration. This amounts to over 800 hours of sensor data including acceleration, gyroscope, compass, wifi and sound annotated with groundtruth at less than 1min resolution

Identification of stably expressed reference small non-coding RNAs for microRNA quantification in high-grade serous ovarian carcinoma tissues

MicroRNAs (miRNAs) belong to a family of small non‐coding RNAs (sncRNAs) playing important roles in human carcinogenesis. Multiple investigations reported miRNAs aberrantly expressed in several cancers, including high‐grade serous ovarian carcinoma (HGS‐OvCa). Quantitative PCR is widely used in studies investigating miRNA expression and the identification of reliable endogenous controls is crucial for proper data normalization. In this study, we aimed to experimentally identify the most stable reference sncRNAs for normalization of miRNA qPCR expression data in HGS‐OvCa. Eleven putative reference sncRNAs for normalization (U6, SNORD48, miR‐92a‐3p, let‐7a‐5p, SNORD61, SNORD72, SNORD68, miR‐103a‐3p, miR‐423‐3p, miR‐191‐5p, miR‐16‐5p) were analysed on a total of 75 HGS‐OvCa and 30 normal tissues, using a highly specific qPCR. Both the normal tissues considered to initiate HGS‐OvCa malignant transformation, namely ovary and fallopian tube epithelia, were included in our study. Stability of candidate endogenous controls was evaluated using an equivalence test and validated by geNorm and NormFinder algorithms. Combining results from the three different statistical approaches, SNORD48 emerged as stably and equivalently expressed between malignant and normal tissues. Among malignant samples, considering groups based on residual tumour, miR‐191‐5p was identified as the most equivalent sncRNA. On the basis of our results, we support the use of SNORD48 as best reference sncRNA for relative quantification in miRNA expression studies between HGS‐OvCa and normal controls, including the first time both the normal tissues supposed to be HGS‐OvCa progenitors. In addition, we recommend miR‐191‐5p as best reference sncRNA in miRNA expression studies with prognostic intent on HGS‐OvCa tissues

ramble on tracing movements of popular historical figures

We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century

Evaluation of a novel human IgG1 anti-claudin3 antibody that specifically recognizes its aberrantly localized antigen in ovarian cancer cells and that is suitable for selective drug delivery

Membrane protein claudin3 has been recently suggested as a marker for biologically aggressive tumors and a possible target for the therapeutic delivery of active anti-cancer compounds. Claudin3-binding molecules such as the Clostridium perfringens enterotoxin (CPE), CPE-related molecules, and murine and chimeric antibodies have shown promising antitumor efficacy in preclinical oncological settings. We first engineered a fully human anti-claudin3 IgG1 antibody (IgGH6) by fusing the human IgG1 Fc-domain to the anti-claudin3 scFvH6 previously isolated from a pre-immune phage display library. The construct was expressed in mammalian cells and specifically targeted claudin3 endogenously expressed on the surface of different human ovarian cancer cell lines. No detectable cross-reactivity with other homologous claudins was observed. The epitope recognized by IgGH6 is located within the minor extracellular domain of claudin3 and becomes accessible only in tumor cells characterized by incomplete junction formation. Confocal microscopy experiments demonstrated that IgGH6 was actively internalized in tumor cells after binding to native claudin3 and co-localized, likely within intracellular vesicles, with the C-CPE peptide. Preliminary results indicate that IgGH6 accumulated in vivo in free claudin3 ovarian carcinoma xenografts. For its selective uptake in tumor cells and its human nature, IgGH6 represents a valuable candidate for antibody-drug conjugate therapeutic applications in ovarian cancer patients

Semantic Name Matching

Names are studied in different fields, and, among the issues they present,name variations (e.g., translations, misspellings, etc...) and name variants (e.g., pseudonyms) pose a challenge to name matching, i.e., discovering instances that differ typographically but represent the same entity. Our scenario for name matching is a P2P, entity-based network of users divided in local level (the users), community level (groups of users), and global level(all the entities). Entities at local level are a partial view of the real word entity, represented at the global level. In this framework, name variations and name variants change the orthography of names because of linguistic and social factors, and their presence depends on the scenario level considered. Thus, they are hard to tackle by an automatic approach such as name matching. Our proposed solutions is to use a taxonomy we created to understand and predict the variations and variants of different entity names, and divide the entity name in different entries to accommodate the original name plus variations and variants. Our approach is novel because we take advantage of a multidisciplinary method, drawing from various fields (i.e., philosophy, sociology and geography) importing terms and views not found in computer science. We also draw from areas close to name matching, building from their findings and expanding them

Ramble on: tracing movements of popular historical figures

We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century

RAMBLE ON: Tracing Movements of Popular Historical Figures

We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century