Practical Management of the Endangered African Wild Dog (Lycaon Pictus): Implementation of Research Methodologies, Management Tools and Validation of Non-Invasive Endocrinology Applications

The porpoise of this study was to implement research methodologies and assess the effectiveness and impact of management tools to promote best practices for the long term conservation of the endangered African wild dog (Lycaon pictus). Different methods were included in the project framework to investigate and expand the applicability of these methodologies to free-ranging African wild dogs in the southern African region: ethology, behavioural endocrinology and ecology field methodologies were tested and implemented. Additionally, research was performed to test the effectiveness and implication of a contraceptive implant (Suprenolin) as a management tool for the species of a subpopulation hosted in fenced areas. Attention was especially given to social structure and survival of treated packs. This research provides useful tools and advances the applicability of these methods for field studies, standardizing and improving research instruments in the field of conservation biology and behavioural endocrinology. Results reported here provide effective methodologies to expand the applicability of non-invasive endocrine assessment to previously prohibited fields, and validation of sampling methods for faecal hormone analysis. The final aim was to fill a knowledge gap on behaviours of the species and provide a common ground for future researchers to apply non-invasive methods to this species research and to test the effectiveness of the contraception on a managed metapopulation.

Management Perspectives on Industrial Heritage: an Exploratory Research

The current research project focuses on the analysis of the critical issues of industrial heritage management in Italy and the preservation of organizational history within the reuse projects of former industrial sites. The organizational and managerial perspective is crucial on two levels. Firstly, it is important in the analysis of the original significance of the site, and in particular its organizational history, and its conservation within the new regeneration strategy. Secondly, it is crucial at the phase of management of reuse projects and its feasibility and sustainability analysis. Based on the analysis of the literature, a unique classification of the reuse strategies that can be implemented in order to regenerate former industrial sites has been formulated. The exploratory research thus adapts a multiple case study design. Eight Italian case studies have been chosen, one for each type of regeneration strategy. Each case study is explored as a stand-alone entity through the analysis of the local differences and idiosyncrasies of the specific context, the factors that stood behind the choice of the reuse strategy and the way the reuse project evolved through the years. Then, the current management of each reuse project is analysed. The narration and musealization of the organizational history is investigated through the spatial dimension, the level of content and the level of expression. Secondly, the case studies are compared through a cross-case analysis from three different perspectives: issues on the phase of preparation and implementation of the reuse projects, critical issues behind the current management of new projects and issues on the ways of preservation and narration of organizational history within the new project. The research shows that all regeneration strategies are affected by the conflict between preservation and change, by the issue of materiality and selectivity.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Research of predictive biomarkers to anti-CD22 antibody-drug conjugate treatment in B-cell acute lymphoblastic leukemia

Background: The treatment of B-cell acute lymphoblastic leukemia (B-ALL) has been enriched by novel agents targeting surface markers CD19 and CD22. Inotuzumab ozogamicin (INO) is a CD22-calicheamicin conjugated monoclonal antibody approved in the setting of relapse/refractory (R/R) B-ALL able to induce a high rate of deep responses, not durable over time. Aims: This study aims to identify predictive biomarkers to INO treatment in B- ALL by flow cytometric analysis of CD22 expression and gene expression profile. Materials and methods: Firstly, the impact on patient outcome in 30 R/R B-ALL patients of baseline CD22 expression in terms of CD22 blast percentage and CD22 fluorescent intensity (CD22-FI) was explored. Secondly, baseline gene expression profile of 18 R/R B-ALL patient samples was analyzed. For statistical analysis of differentially expressed genes (DEGs) patients were divided in non-responders (NR), defined as either INO-refractory or with duration of response (DoR) < 3 months, and responders (R). Gene expression results were analyzed with Ingenuity pathway analysis (IPA). Results: In our patient set higher CD22-FI, defined as higher quartiles (Q2-Q4), correlated with better patient outcome in terms of CR rate, OS and DoR, compared to lower CD22-FI (Q1). CD22 blast percentage was less able to discriminate patients’ outcome, although a trend for better outcome in patients with CD22 ≥ 90% could be appreciated. Concerning gene expression profile, 32 genes with corrected p value <0.05 and absolute FC ≥2 were differentially expressed in NR as compared to R. IPA upstream regulator and regulator effect analysis individuated the inhibition of tumor suppressor HIPK2 as causal upstream condition of the downregulation of 6 DEGs. Conclusions: CD22-FI integrates CD22-percentage on leukemic blasts for a more comprehensive target pre-treatment evaluation. Moreover, a unique pattern of gene expression signature based on HIPK2 downregulation was identified, providing important insights in mechanisms of resistance to INO.

RICORDACI: Research on the Conservation, Restoration and Diagnosis of Cinematographic Films

This thesis is the result of the RICORDACI project, a three-year European-funded initiative involving the collaboration between the University of Bologna and the restoration laboratory of the Cineteca di Bologna, L'immagine Ritrovata, which aimed to develop innovative solutions and technologies for the preservation of cinematographic film heritage. In particular, this thesis presents new analytical methodologies to exploit two types of portable miniaturized Near Infrared spectrometers working in Diffuse Reflectance over the Short Wave Infrared (SWIR) range, to study the near infrared (NIR) spectral behavior of film base materials for an accurate, non-invasive and fast characterization of the polymer type; and for films with cellulose acetate supports, they can be employed as a diagnostic tool for monitoring the Degree of substitution (DS) affected by the loss of acetyl groups. The proposed methods offer non-invasive, fast, inexpensive and simple alternatives for the characterization and diagnosis of film bases to help the strategic planning and decision-making regarding storage, digitalization and intervention of film collections. Secondly, the thesis includes the evaluation of new green cleaning systems and solvents for the effective, fast and innocuous removal of undesired substances from degraded cinematographic films bases; these tests compared the efficiency of traditional systems and solvents against the new proposals. Firstly, the use of Deep Eutectic Solvent formulations for removing softened gelatin residues from cellulose nitrate bases; and secondly, the employment of green volatile solvents with different application methods, including the use of new electrospun nylon mats, for avoiding the dangerous use of friction for the removal of Triphenyl Phosphate blooms from the surface of cellulose acetate bases. The results obtained will help improving the efficiency of the interventions needed before the digitalization of historical cinematographic films and will pave the way for further investigation on the use of green solvents for cleaning polymeric heritage objects.

The European legal approach to Open Science and research data

This dissertation proposes an analysis of the governance of the European scientific research, focusing on the emergence of the Open Science paradigm: a new way of doing science, oriented towards the openness of every phase of the scientific research process, able to take full advantage of the digital ICTs. The emergence of this paradigm is relatively recent, but in the last years it has become increasingly relevant. The European institutions expressed a clear intention to embrace the Open Science paradigm (eg., think about the European Open Science Cloud, EOSC; or the establishment of the Horizon Europe programme). This dissertation provides a conceptual framework for the multiple interventions of the European institutions in the field of Open Science, addressing the major legal challenges of its implementation. The study investigates the notion of Open Science, proposing a definition that takes into account all its dimensions related to the human and fundamental rights framework in which Open Science is grounded. The inquiry addresses the legal challenges related to the openness of research data, in light of the European Open Data framework and the impact of the GDPR on the context of Open Science. The last part of the study is devoted to the infrastructural dimension of the Open Science paradigm, exploring the e-infrastructures. The focus is on a specific type of computational infrastructure: the High Performance Computing (HPC) facility. The adoption of HPC for research is analysed from the European perspective, investigating the EuroHPC project, and the local perspective, proposing the case study of the HPC facility of the University of Luxembourg, the ULHPC. This dissertation intends to underline the relevance of the legal coordination approach, between all actors and phases of the process, in order to develop and implement the Open Science paradigm, adhering to the underlying human and fundamental rights.

Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Patient data sharing for the improvement of healthcare and medical research: towards a moral duty of patients to share health-related data?

The project answers to the following central research question: ‘How would a moral duty of patients to transfer (health) data for the benefit of health care improvement, research, and public health in the eHealth sector sit within the existing confidentiality, privacy, and data protection legislations?’. The improvement of healthcare services, research, and public health relies on patient data, which is why one might raise the question concerning a potential moral responsibility of patients to transfer data concerning health. Such a responsibility logically would have subsequent consequences for care providers concerning the further transferring of health data with other healthcare providers or researchers and other organisations (who also possibly transfer the data further with others and other organisations). Otherwise, the purpose of the patients’ moral duty, i.e. to improve the care system and research, would be undermined. Albeit the arguments that may exist in favour of a moral responsibility of patients to share health-related data, there are also some moral hurdles that come with such a moral responsibility. Furthermore, the existing European and national confidentiality, privacy and data protection legislations appear to hamper such a possible moral duty, and they may need to be reconsidered to unlock the full use of data for healthcare and research.

Big data and AI applications for health and research. Co-regulation mechanisms as a prosed solution for data protection law issues

Big data and AI are paving the way to promising scenarios in clinical practice and research. However, the use of such technologies might clash with GDPR requirements. Today, two forces are driving the EU policies in this domain. The first is the necessity to protect individuals’ safety and fundamental rights. The second is to incentivize the deployment of innovative technologies. The first objective is pursued by legislative acts such as the GDPR or the AIA, the second is supported by the new data strategy recently launched by the European Commission. Against this background, the thesis analyses the issue of GDPR compliance when big data and AI systems are implemented in the health domain. The thesis focuses on the use of co-regulatory tools for compliance with the GDPR. This work argues that there are two level of co-regulation in the EU legal system. The first, more general, is the approach pursued by the EU legislator when shaping legislative measures that deal with fast-evolving technologies. The GDPR can be deemed a co-regulatory solution since it mainly introduces general requirements, which implementation shall then be interpretated by the addressee of the law following a risk-based approach. This approach, although useful is costly and sometimes burdensome for organisations. The second co-regulatory level is represented by specific co-regulatory tools, such as code of conduct and certification mechanisms. These tools are meant to guide and support the interpretation effort of the addressee of the law. The thesis argues that the lack of co-regulatory tools which are supposed to implement data protection law in specific situations could be an obstacle to the deployment of innovative solutions in complex scenario such as the health ecosystem. The thesis advances hypothesis on theoretical level about the reasons of such a lack of co-regulatory solutions.

Musculoskeletal disorders, research quality, and pelvic floor health in sports: exploring current evidence and new perspectives

Background. Pelvic floor dysfunction (PFD) is an umbrella term that includes a myriad of conditions such as urinary (UI) and anal incontinence, pelvic organ prolapse, pelvic pain, and sexual dysfunction. Literature showed high prevalence rates of PFD among athletes, especially UI, with high-impact sports have been linked with an increased risk of developing symptoms. However, comprehensive research summarising PFD prevalence across sexes, exploring treatment options, and the absence of a standardised referral screening tool are notable gaps. Misinformation is also prevalent in the sports medicine field. Methods. This doctoral project comprises four studies addressing different aspects of pelvic health in athletes. The first two studies were scoping reviews of epidemiological PFD data in male and female athletes, as well as available interventions. Study 3 concerned the development of a new screening tool for PFD in female athletes, aiming to guide sports medicine clinicians in referring patients to PFD specialists through a worldwide Delphi consensus. Study 4 summarised all previous findings, integrating data into an infographic. Results and conclusions. In Study 1, the findings of 100 articles on PFD in both sexes have been collected, highlighting a higher prevalence of studies on female athletes evaluating UI across multiple sports. Other conditions remain rarely investigated. Study 2 found a diverse range of interventions for female PFD, with a notable emphasis on conservative approaches. Recommendations for clinical practice often relied on the transferability of results from the nonathlete population or expert opinions. In Study 3, 41 international experts took part in the consensus development of the Pelvic Floor Dysfunction-ScrEeNing Tool IN fEmale athLetes (PFD-SENTINEL). It incorporates a cluster of PFD symptoms, items (risk factors, clinical, and sports-related characteristics), and a clinical algorithm. Lastly, Study 4 included ten evidence-based information with a relative description concerning pelvic floor health in athletes.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.

Towards Europe 2050: the role of institutions, EU research, and citizens for climate justice. The urban challenge in Amsterdam, Bilbao, Freiburg, and Lisbon.

The climate crisis is the greatest challenge humanity has ever faced, and in 2023 the average global temperature reached new records, prompting the UN Secretary General to declare that 'the era of global warming is over, and the era of global boiling has arrived'. In this context, urban areas play a key role, and can be considered a bottleneck of the climate crisis. The European Commission is investing billions of euros in research and innovation projects in urban areas, while the European Green Deal strategy has the ambition of making Europe the first carbon-neutral continent on the planet by 2050. However, studies and research show that the causes of the climate crisis are rooted in an economic system that produces profound inequalities, and the very solutions to address the consequences of global warming risk deepening them. In this context, the role of cities is not only to decarbonise their urban fabric, but to build solutions to the social challenge posed by the climate crisis, promoting paradigm shifts capable of producing trajectories towards so-called 'climate justice'. This research analyses, through a holistic view, European policies in these fields, and delves into the actions and projects of four European cities - Amsterdam, Bilbao, Freiburg, and Lisbon - through a qualitative approach aimed at identifying strengths and contradictions of strategies to tackle the climate crisis. Delving into the collective dynamics and social impacts of the actions promoted, the research proposes a comprehensive view of the role that urban areas can play not only in decarbonising society, but in promoting a paradigm shift capable of addressing the economic causes and social consequences of the climate crisis.

Boys In White: A Classic Of Qualitative Research Turns 50.

This article analyzes Boys in white: student culture in medical schoolby Howard S. Becker, Blanche Geer, Everett C. Hughes and Anselm Strauss, considered a model of qualitative research in sociology. The analysis investigates the trajectories of the authors, the book, qualitative analysis, and the medical students, emphasizing their importance in the origins of medical sociology and the sociology of medical education. In the trajectory of the authors, bibliographical information is given. The trajectory of qualitative research focuses on how this methodology influences the construction of the field. The investigation of the students' trajectory shows how they progress through their first years at medical school to build their own student culture.

[strategies For Tackling Absenteeism In Dental Appointments In The Family Health Units Of A Large Municipality: Action Research].

The aim of this study was to analyze the reasons for missed appointments in dental Family Health Units (FHU) and implement strategies to reduce same through action research. This is a study conducted in 12 FHUs in Piracicaba in the State of São Paulo from January, 1 to December, 31 2010. The sample was composed of 385 users of these health units who were interviewed over the phone and asked about the reasons for missing dental appointments, as well as 12 dentists and 12 nurses. Two workshops were staged with professionals: the first to assess the data collected in interviews and develop strategy, and the second for evaluation after 4 months. The primary cause for missed appointments was the opening hours of the units coinciding with the work schedule of the users. Among the strategies suggested were lectures on oral health, ongoing education in team meetings, training of Community Health Agents, participation in therapeutic groups and partnerships between Oral Health Teams and the social infrastructure of the community. The adoption of the single medical record was the strategy proposed by professionals. The strategies implemented led to a 66.6% reduction in missed appointments by the units and the motivating nature of the workshops elicited critical reflection to redirect health practices.