To be or not to be stable, that is the question: understanding neural networks for inverse problems

The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem, since the ill-conditioning amplifies the noise on the data. Recently introduced deep-learning based algorithms overwhelm the more traditional model-based approaches but they typically suffer from instability with respect to data perturbation. In this paper, we theoretically analyse the trade-off between neural networks stability and accuracy in the solution of linear inverse problems. Moreover, we propose different supervised and unsupervised solutions, to increase network stability by maintaining good accuracy, by inheriting, in the network training, regularization from a model-based iterative scheme. Extensive numerical experiments on image deblurring confirm the theoretical results and the effectiveness of the proposed networks in solving inverse problems with stability with respect to noise.Comment: 26 pages, 9 figures, divided in 4 blocks of figures in the LaTeX code. Paper will be sent for publication on a journal soon. This is a preliminary version, updated versions will be uploaded on ArXi

PCSK9 Biology and Its Role in Atherothrombosis

It is now about 20 years since the first case of a gain-of-function mutation involving the as-yet-unknown actor in cholesterol homeostasis, proprotein convertase subtilisin/kexin type 9 (PCSK9), was described. It was soon clear that this protein would have been of huge scientific and clinical value as a therapeutic strategy for dyslipidemia and atherosclerosis-associated cardiovascular disease (CVD) management. Indeed, PCSK9 is a serine protease belonging to the proprotein convertase family, mainly produced by the liver, and essential for metabolism of LDL particles by inhibiting LDL receptor (LDLR) recirculation to the cell surface with the consequent upregulation of LDLR-dependent LDL-C levels. Beyond its effects on LDL metabolism, several studies revealed the existence of additional roles of PCSK9 in different stages of atherosclerosis, also for its ability to target other members of the LDLR family. PCSK9 from plasma and vascular cells can contribute to the development of atherosclerotic plaque and thrombosis by promoting platelet activation, leukocyte recruitment and clot formation, also through mechanisms not related to systemic lipid changes. These results further supported the value for the potential cardiovascular benefits of therapies based on PCSK9 inhibition. Actually, the passive immunization with anti-PCSK9 antibodies, evolocumab and alirocumab, is shown to be effective in dramatically reducing the LDL-C levels and attenuating CVD. While monoclonal antibodies sequester circulating PCSK9, inclisiran, a small interfering RNA, is a new drug that inhibits PCSK9 synthesis with the important advantage, compared with PCSK9 mAbs, to preserve its pharmacodynamic effects when administrated every 6 months. Here, we will focus on the major understandings related to PCSK9, from its discovery to its role in lipoprotein metabolism, involvement in atherothrombosis and a brief excursus on approved current therapies used to inhibit its action

Can Telematics Improve Driving Style? The Use of Behavioural Data in Motor Insurance

The use of behavioural data in insurance is loaded with promises and unresolved issues. This paper explores the related opportunities and challenges analysing the use of telematics data in third-party liability motor insurance. Behavioural data are used not only to refine the risk profile of policyholders, but also to implement innovative coaching strategies, feeding back to the drivers the aggregated information obtained from the data. The purpose is to encourage an improvement in their driving style. Our research explores the effectiveness of coaching on the basis of an empirical investigation of the dataset of a company selling telematics motor insurance policies. The results of our quantitative analysis show that this effectiveness crucially depends on the propensity of policyholders to engage with the telematics app. We observe engagement as an additional kind of behaviour, producing second-order behavioural data that can also be recorded and strategically used by insurance companies. The conclusions discuss potential advantages and risks connected with this extended interpretation of behavioural data.Comment: Paper sent for publication on a journal. This is a preliminary version, updated versions will be uploade

Exploiting fashion x-commerce through the empowerment of voice in the fashion virtual reality arena. Integrating voice assistant and virtual reality technologies for fashion communication

The ongoing development of eXtended Reality (XR) technologies is supporting a rapid increase of their performances along with a progressive decrease of their costs, making them more and more attractive for a large class of consumers. As a result, their widespread use is expected within the next few years. This may foster new opportunities for e-commerce strategies, giving birth to an XR-based commerce (x-commerce) ecosystem. With respect to web and mobile-based shopping experiences, x-commerce could more easily support brick-and-mortar store-like experiences. One interesting and consolidated one amounts to the interactions among customers and shop assistants inside fashion stores. In this work, we concentrate on such aspects with the design and implementation of an XR-based shopping experience, where vocal dialogues with an Amazon Alexa virtual assistant are supported, to experiment with a more natural and familiar contact with the store environment. To verify the validity of such an approach, we asked a group of fashion experts to try two different XR store experiences: with and without the voice assistant integration. The users are then asked to answer a questionnaire to rate their experiences. The results support the hypothesis that vocal interactions may contribute to increasing the acceptance and comfortable perception of XR-based fashion shopping

Perceptions of Caring Behavior Among Undergraduate Nursing Students: A Three-Cohort Observational Study

Introduction: Increase in the knowledge of “caring science” among nurses plays a key role in ensuring a correct caring behavior towards patients. Caring training for students is a priority in nursing education, but unfortunately there are limited and conflicting studies which explore this outcome. The purpose of this observational study was to explore the perceptions of caring behaviors by nursing students during their clinical practice training in order to highlight if the level of caring behaviors changes as the nursing course progresses. Materials and Methods: The Caring Behaviors Inventory-24 (CBI-24) was administered to 331 students, enrolled in the three years of an Italian Nursing Course, who accepted to participate in the study (89.2% response rate). The data were analyzed using SPSS software version 26.0 (SPSS Inc., Chicago, IL). Results: The total mean score of CBI-24 was 4.82 in the first, 5.12 in the second and 5.26 in the third-year students. The CBI-24 dimensions “Responding to individual needs” and “Being with” obtained the highest scores among the students of the first year. At the end of the first year, our students were already able to perform expressive caring, whereas instrumental caring developed at a high level in the second and third years. We did not highlight any statistically significant difference between the two gender CBI-24 item scores. Conclusion: In light of our results, we put in evidence that Nursing Degree Programme favours the development in students of both relational and technical components of caring behaviors. We hope that in future students’ self-assessment of caring behaviors could be considered an educational outcome for Nursing Programme

Nursing student attitudes toward dying patient care: A European multicenter cross-sectional study

Background and aim of the work: Nursing education plays a key role in preparing future nurses to deal with dying patients, which represents one of the most emotionally involving aspect of nursing. The aims of the study were to explore nursing students’ attitudes towards care of dying patients in three different European contexts and to analyze the variables that can influence them. Methods: We conducted an international mul- ticenter cross-sectional study. We administered the Frommelt Attitude Toward Care of the Dying Scale form B (FATCOD-B) and a demographic form to 569 students, enrolled in three Nursing Programmes in different countries (Italy, Spain and United Kingdom), who accepted to participate in the study. The data were analyzed using SPSS software version 26.0. Results: Median total FATCOD-B scores indicated intermediate levels of students’ attitudes towards care for dying patients, with a statistically significant difference among the three student groups. The median total FATCOD-B scores did not statistically significantly change in students with different age, gender, year of study, religious beliefs, nursing education on palliative care, previous expe- riences of dying patient care and personal grieving. Conclusions: In our study, nursing students feel partially prepared in caring for dying patients and their attitudes do not change as the course of study progresses. No selected variables had an impact on students’ attitudes towards palliative care. Since nurses play a vital role in ensuring the quality of care, education on end-of-life care should be offered as a core part of undergraduate nursing programs

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival