Promoting data provenance tracking in the archaeological interpretation process

n this paper we propose a model and a set of derivation rules for tracking data provenance during the archaeological interpretation process. The interpretation process is the main task performed by an archaeologist that, starting from ground data about evidences and findings, tries to derive knowledge about an ancient object or event. In particular, in this work we concentrate on the dating process used by archaeologists to assign one or more time intervals to a finding in order to define its lifespan on the temporal axis and we propose a framework to represent such information and infer new knowledge including provenance of data. Archaeological data, and in particular their temporal dimension, are typically vague, since many different interpretations can coexist, thus we will use Fuzzy Logic to assign a degree of confidence to values and Fuzzy Temporal Constraint Networks to model relationships between dating of different finding

Top-N recommendations on Unpopular Items with Contextual Knowledge

Traditional recommender systems provide recommendations of items to users; recently, some of them also consider the context related to predictions. In this paper we propose a technique that relies on classical recommendation algorithms and post-filters recommendations on the basis of contextual information available for them. Association rules are exploited to identify the most significant correlations among context and item characteristics. The mined rules are used to filter the predictions performed by traditional recommender systems to provide contextualized recommendations. Our experimental results show that the proposed approach allows improving the output of classical algorithms proposed in the literature, especially in the case of unpopular items

A data mining approach to incremental adaptive functional diagnosis

This paper presents a novel approach to functional fault diagnosis adopting data mining to exploit knowledge extracted from the system model. Such knowledge puts into relation test outcomes with components failures, to define an incremental strategy for identifying the candidate faulty component. The diagnosis procedure is built upon a set of sorted, possibly approximate, rules that specify given a (set of) failing test, which is the faulty candidate. The procedure iterative selects the most promising rules and requests the execution of the corresponding tests, until a component is identified as faulty, or no diagnosis can be performed. The proposed approach aims at limiting the number of tests to be executed in order to reduce the time and cost of diagnosis. Results on a set of examples show that the proposed approach allows for a significant reduction of the number of executed tests (the average improvement ranges from 32% to 88%)

What is the role of context in fair group recommendations?

We investigate the role played by the context, i.e. the situation the group is currently experiencing, in the design of a system that recommends sequences of activities as a multi-objective optimization problem, where the satisfaction of the group and the available time interval are two of the functions to be optimized. In particular, we highlight that the dynamic evolution of the group can be the key contextual feature that has to be considered to produce fair suggestions

A context-based approach for partitioning big data

In recent years, the amount of available data keeps growing at fast rate, and it is therefore crucial to be able to process them in an efficient way. The level of parallelism in tools such as Hadoop or Spark is determined, among other things, by the partitioning applied to the dataset. A common method is to split the data into chunks considering the number of bytes. While this approach may work well for text-based batch processing, there are a number of cases where the dataset contains structured information, such as the time or the spatial coordinates, and one may be interested in exploiting such a structure to improve the partitioning. This could have an impact on the processing time and increase the overall resource usage efficiency. This paper explores an approach based on the notion of context, such as temporal or spatial information, for partitioning the data. We design a context-based multi-dimensional partitioning technique that divides an n 12dimensional space into splits by considering the distribution of the each contextual dimension in the dataset. We tested our approach on a dataset from a touristic scenario, and our experiments show that we are able to improve the efficiency of the resource usage

Economic analysis of remote monitoring in patients with implantable cardioverter defibrillators or cardiac resynchronization therapy defibrillators in the Trento area, Italy

Introduction: Remote monitoring (RM) technologies have the potential to improve patient care by increasing compliance, providing early indications of heart failure (HF), and potentially allowing for therapy optimization to prevent HF admissions. The aim of this retrospective study was to assess the clinical and economic consequences of RM vs. standard monitoring (SM) through in-office cardiology visits, in patients carrying a cardiac implantable electronic device (CIED). Methods: Clinical and resource consumption data were extracted from the Electrophysiology Registry of the Trento Cardiology Unit, which has been systemically collecting patient information from January 2011 to February 2022. From a clinical standpoint, survival analysis was conducted, and incidence of cardiovascular (CV) related hospitalizations was measured. From an economic standpoint, direct costs of RM and SM were collected to compare the cost per treated patient over a 2-year time horizon. Propensity score matching (PSM) was used to reduce the effect of confounding biases and the unbalance of patient characteristics at baseline. Results: In the enrollment period, N = 402 CIED patients met the inclusion criteria and were included in the analysis (N = 189 patients followed through SM; N = 213 patients followed through RM). After PSM, comparison was limited to N = 191 patients in each arm. After 2-years follow-up since CIED implantation, mortality rate for any cause was 1.6% in the RM group and 19.9% in the SM group (log-rank test, p < 0.0001). Also, a lower proportion of patients in the RM group (25.1%) were hospitalized for CV-related reasons, compared to the SM group (51.3%; p < 0.0001, two-sample test for proportions). Overall, the implementation of the RM program in the Trento territory was cost-saving in both payer and hospital perspectives. The investment required to fund RM (a fee for service in the payer perspective, and staffing costs for hospitals), was more than offset by the lower rate of hospitalizations for CV-related disease. RM adoption generated savings of −€4,771 and −€6,752 per patient in 2 years, in the payer and hospital perspective, respectively. Conclusion: RM of patients carrying CIED improves short-term (2-years) morbidity and mortality risks, compared to SM and reduces direct management costs for both hospitals and healthcare services

Autosomal Recessive Atrial Dilated Cardiomyopathy With Standstill Evolution Associated With Mutation of Natriuretic Peptide Precursor A

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Choice of costimulatory domains and of cytokines determines CAR T-cell activity in neuroblastoma

Chimeric antigen receptor (CAR) T-cell therapy has been shown to be dramatically effective in the treatment of B-cell malignancies. However, there are still substantial obstacles to overcome, before similar responses can be achieved in patients with solid tumors. We evaluated both in vitro and in a preclinical murine model the efficacy of different 2nd and 3rd generation CAR constructs targeting GD2, a disial-ganglioside expressed on the surface of neuroblastoma (NB) tumor cells. In order to address potential safety concerns regarding clinical application, an inducible safety switch, namely inducible Caspase-9 (iC9), was also included in the vector constructs. Our data indicate that a 3rd generation CAR incorporating CD28.4-1BB costimulatory domains is associated with improved anti-tumor efficacy as compared with a CAR incorporating the combination of CD28.OX40 domains. We demonstrate that the choice of 4-1BB signaling results into significant amelioration of several CAR T-cell characteristics, including: 1) T-cell exhaustion, 2) basal T-cell activation, 3) in vivo tumor control and 4) T-cell persistence. The fine-tuning of T-cell culture conditions obtained using IL7 and IL15 was found to be synergic with the CAR.GD2 design in increasing the anti-tumor activity of CAR T cells. We also demonstrate that activation of the suicide gene iC9, included in our construct without significantly impairing neither CAR expression nor anti-tumor activity, leads to a prompt induction of apoptosis of GD2.CAR T cells. Altogether, these findings are instrumental in optimizing the function of CAR T-cell products to be employed in the treatment of children with NB

Different Innate and Adaptive Immune Responses to SARS-CoV-2 Infection of Asymptomatic, Mild, and Severe Cases

SARS-CoV-2 is a novel coronavirus, not encountered before by humans. The wide spectrum of clinical expression of SARS-CoV-2 illness suggests that individual immune responses to SARS-CoV-2 play a crucial role in determining the clinical course after first infection. Immunological studies have focused on patients with moderate to severe disease, demonstrating excessive inflammation in tissues and organ damage. In order to understand the basis of the protective immune response in COVID-19, we performed a longitudinal follow-up, flow-cytometric and serological analysis of innate and adaptive immunity in 64 adults with a spectrum of clinical presentations: 28 healthy SARS-CoV-2-negative contacts of COVID-19 cases; 20 asymptomatic SARS-CoV-2-infected cases; eight patients with Mild COVID-19 disease and eight cases of Severe COVID-19 disease. Our data show that high frequency of NK cells and early and transient increase of specific IgA, IgM and, to a lower extent, IgG are associated with asymptomatic SARS-CoV-2 infection. By contrast, monocyte expansion and high and persistent levels of IgA and IgG, produced relatively late in the course of the infection, characterize severe disease. Modest increase of monocytes and different kinetics of antibodies are detected in mild COVID-19. The importance of innate NK cells and the short-lived antibody response of asymptomatic individuals and patients with mild disease suggest that only severe COVID-19 may result in protective memory established by the adaptive immune response