JKarma: A Highly-Modular Framework for Pattern-Based Change Detection on Evolving Data

Pattern-based change detection (PBCD) describes a class of change detection algorithms for evolving data. Contrary to conventional solutions, PBCD seeks changes exhibited by the patterns over time and therefore works on an abstract form of the data, which prevents the search for changes on the raw data. Moreover, PBCD provides arguments on the validity of the results because patterns mirror changes occurred with any form of evidence. However, the existing solutions differ on data representation, mining algorithm and change identification strategy, which we can deem as main modules of a general architecture, so that any PBCD task could be designed by accommodating custom implementations for those modules. This is what we propose in this paper through jKarma, a highly-modular framework for designing and performing PBCD

Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

Urban pollution is usually monitored via fixed stations that provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Currently, the spread of mobile devices has fostered the development of new approaches, like Mobile Crowd Sensing (MCS), increasing the chances of using smartphones as suitable sensors in the urban monitoring scenario, because it potentially contributes massive ubiquitous data at relatively low cost. However, MCS is useless (or even counter-productive), if contributed data are not trustworthy, due to wrong data-collection procedures by non-expert practitioners. Contextualizing monitored data with those coming from phone-embedded sensors and from time/space proximity can improve data trustworthiness. This work focuses on the development of an algorithm that exploits context awareness to improve the reliability of MCS collected data. It has been validated against some real use cases for noise pollution and promises to improve the trustworthiness of end users generated data

Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19)

Determinants of frontline tyrosine kinase inhibitor choice for patients with chronic-phase chronic myeloid leukemia: A study from the Registro Italiano LMC and Campus CML

BackgroundImatinib, dasatinib, and nilotinib are tyrosine kinase inhibitors (TKIs) approved in Italy for frontline treatment of chronic-phase chronic myeloid leukemia (CP-CML). The choice of TKI is based on a combined evaluation of the patient's and the disease characteristics. The aim of this study was to analyze the use of frontline TKI therapy in an unselected cohort of Italian patients with CP-CML to correlate the choice with the patient's features. MethodsA total of 1967 patients with CP-CML diagnosed between 2012 and 2019 at 36 centers throughout Italy were retrospectively evaluated; 1089 patients (55.4%) received imatinib and 878 patients (44.6%) received a second-generation (2G) TKI. ResultsSecond-generation TKIs were chosen for most patients aged <45 years (69.2%), whereas imatinib was used in 76.7% of patients aged >65 years (p < .001). There was a predominant use of imatinib in intermediate/high European long-term survival risk patients (60.0%/66.0% vs. 49.7% in low-risk patients) and a limited use of 2G-TKIs in patients with comorbidities such as hypertension, diabetes, chronic obstructive pulmonary disease, previous neoplasms, ischemic heart disease, or stroke and in those with >3 concomitant drugs. We observed a greater use of imatinib (61.1%) in patients diagnosed in 2018-2019 compared to 2012-2017 (53.2%; p = .002). In multivariable analysis, factors correlated with imatinib use were age > 65 years, spleen size, the presence of comorbidities, and & GE;3 concomitant medications. ConclusionsThis observational study of almost 2000 cases of CML shows that imatinib is the frontline drug of choice in 55% of Italian patients with CP-CML, with 2G-TKIs prevalently used in younger patients and in those with no concomitant clinical conditions. Introduction of the generic formulation in 2018 seems to have fostered imatinib use

Influence of time to complete remission and duration of all-trans retinoic acid therapy on the relapse risk in patients with acute promyelocytic leukemia receiving AIDA protocols

Despite the impressive results obtained with standard chemotherapy, approximately 20% of acute promyelocytic leukemia (APL) patients undergo disease relapse thereby requiring salvage therapy. Few data is available on long-term prognosis in relation to time to complete remission (CR): we reviewed 142 patients treated with AIDA protocols and we found that 42 out of 142 (29.6%) patients achieved CR after 35 days (median time, 42 days). No significant differences in presenting features, including FAB subtype, type of PML/RARA transcript and relapse risk at presentation between the two patient groups achieving CR > or <35 days were revealed, except for male sex and older age that were significantly associated with delayed CR. Rate of relapse was 31% in patients with delayed CR compared to 17% in the group of patients who achieved CR<35 days (p=0.001), with a 5-year CIR of 29.6% compared to 12% (p=0.03). APL patients with delayed CR should be more closely monitored during follow-up for early identification of relapse and prompt administration of pre-emptive salvage therapy

Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature

The discovery of new and potentially meaningful relationships between named entities in biomedical literature can take great advantage from the application of multi-relational data mining approaches in text mining. This is motivated by the peculiarity of multi-relational data mining to be able to express and manipulate relationships between entities. We investigate the application of such an approach to address the task of identifying informative syntactic structures, which are frequent in biomedical abstract corpora. Initially, named entities are annotated in text corpora according to some biomedical dictionary (e.g. MeSH taxonomy). Tagged entities are then integrated in syntactic structures with the role of subject and/or object of the corresponding verb. These structures are represented in a first-order language. Multi-relational approach to frequent pattern discovery allows to identify the verb-based relationships between the named entities which frequently occur in the corpora. Preliminary experiments with a collection of abstracts obtained by querying Medline on a specific disease are reported

Collective Inference for Handling Autocorrelation in Network Regression

In predictive data mining tasks, we should account for autocorrelations of both the independent variables and the dependent variable, which we can observe in neighborhood of a target node and that same node. The prediction on a target node should be based on the value of the neighbours which might even be unavailable. To address this problem, the values of the neighbours should be inferred collectively. We present a novel computational solution to perform collective inferences in a network regression task. We define an iterative algorithm, in order to make regression inferences about predictions of multiple nodes simultaneously and feed back the more reliable predictions made by the previous models in the labeled network. Experiments investigate the effectiveness of the proposed algorithm in spatial network