Mobility and Privacy-Aware Offloading of AR Applications for Healthcare Cyber-Physical Systems in Edge Computing

Cyber-physical systems (CPSs) can be regarded as a new generation of systems which have been widely used for healthcare system. The introduction of Augmented Reality (AR) can further enhance the effectiveness of healthcare CPSs. AR applications can provide a better user experience in the health treatment process for both patients and clinicians. However, AR applications are computation-intensive, putting a substantial computational burden on AR devices. Fortunately, offloading AR applications to edge nodes can enable AR to be suitable for real-time applications. Nevertheless, AR applications deal with the patient’s private information; placing it on edge raises serious privacy concerns. Besides, the network structure of AR applications has spatio-temporal uncertainty. To tackle these issues, we jointly investigate the computation offloading for AR applications in the healthcare CPSs in edge computing considering user privacy protection and mobility. We propose a novel multi-objective meta-heuristic method based on the R2 indicator-II, which preserves privacy, and minimizes the Motion-to-photon latency, energy consumption, and maintain load balancing. Eventually, it verifies the efficiency and superiority of our proposed approach based on a certain scale of the experiments

DataSheet1_A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field.PDF

Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed by Sharma et al. (2013) for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both the R2 statistic and the total standard deviation, together with inter-event and intra-event components.</p

Predictive Medicine for Salivary gland tumours identification through Deep Learning

Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predictive diagnostics is increasingly pervasive; through the use and interpretation of data, the first signs of some diseases (i.e. tumours) can be detected to help physicians make more accurate diagnoses to reduce the errors and develop methods for individualized medical treatment. In this perspective, salivary gland tumours (SGTs) are rare cancers with variable malignancy representing less than 1% of all cancer diagnoses and about 5% of head and neck cancers. The clinical management of SGTs is complicated by a high rate of preclinical diagnostic errors. Today, fine needle aspiration cytology (FNAC) represents the primary diagnostic tool in the hands of clinicians. However, it provides information that about 25% of cases are dubious or inconclusive, complicating therapeutic choices. Thus, finding new tools supporting clinicians to make the right choices in doubtful cases is necessary. This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks

Esigenze Strategiche nella Città Metropolitana di Roma

Il 2016 è un anno cruciale ed ideale per discutere, valutare e ripensare strumenti e metodi orientati al raggiungimento di uno sviluppo sostenibile, coerente, integrato ed equo per il territorio europeo. La nuova Agenda Urbana per la UE (Patto di Amsterdam, 2016), e la Nuova Agenda Urbana (Habitat III, Quito 2016) sono due riferimenti essenziali per la pianificazione urbana e territoriale. Questi documenti, frutto di percorsi partecipativi multi-stakeholder e inter-istituzionali, sintetizzano principi e problemi della pianificazione contemporanea per le città e per le aree metropolitane, e ispirano il disegno di modelli e strumenti che aiutano i pianificatori a gestire ed indirizzare le sfide e cambiamenti dei giorni nostri. Interpretare il cambiamento riuscendo a massimizzarne i risultati sembra uno slogan, ma mai come oggi è il momento di guardare e promuovere lo sviluppo territoriale tenendo conto degli impatti delle politiche europee e globali sulle problematiche urbane e le conseguenti trasformazioni. Le aree urbane e il loro sviluppo in forma inclusiva, responsabile e partecipata sono al centro delle politiche, dei programmi e dei progetti dell’Unione Europea per il corrente periodo di programmazione 2014-20. Aree urbane come potenziale motore trainante la competitività dei territori europei, inseguendo, come ci ricorda la strategia EU 2020, il bisogno di perseguire una crescita sostenibile, inclusiva e smart. Gli obiettivi di coesione, espressi come coesione urbana sociale ed economica, sono orientati ad azioni di coordinamento e integrazione alla base delle quali non può che esserci una visione di lungo termine, fatta di progetti materiali e immateriali, che si fa carico di declinare e assumere gli obiettivi necessari per uno sviluppo sostenibile. In questo ambito, si staglia la pianificazione strategica quale strumento di Governance Urbana Metropolitana necessario e indispensabile, posto a monte della pianificazione di area vasta e urbanistica dei territori. È dai territori, dai soggetti che li reggono e li attraversano, dalle realtà ambientali, economiche, sociali e culturali che li caratterizzano, dalle comunità locali, che prendono forma i piani strategici quali strumenti di indirizzo e progettualità. Grazie a questi strumenti si può arrivare a siglare un patto per lo sviluppo delle stesse comunità. La pianificazione strategica è un processo partecipativo e di coordinamento inter-istituzionale allargato che, nelle sue linee di principio, deve essere in grado di accogliere e ascoltare gli abitanti dei territori e di promuovere e decidere una visione condivisa del futuro, in particolare nelle aree metropolitane, dove strumenti operativi adatti a creare e gestire la governance territoriale, complessa, inter-settoriale e multi-level, specialmente nel contesto italiano, sono ancora da sperimentare. La proposta per una giornata di studi prende avvio da queste sintetiche premesse per conoscere e approfondire esperienze nazionali e internazionali già avviate e in alcuni casi consolidate attorno alle quali avviare un confronto e un percorso di formazione seppure non esaustivo, alla pianificazione strategica, possibile premessa per avviare una nuova stagione di pianificazione territorial

Changes in the incidence of candidemia and related mortality in patients with hematologic malignancies in the last ten years. A SEIFEM 2015-B report

n

Bloodstream infections in haematological cancer patients colonized by multidrug-resistant bacteria

Infections by multidrug-resistant (MDR) bacteria are a worrisome phenomenon in hematological patients. Data on the incidence of MDR colonization and related bloodstream infections (BSIs) in haematological patients are scarce. A multicentric prospective observational study was planned in 18 haematological institutions during a 6-month period. All patients showing MDR rectal colonization as well as occurrence of BSI at admission were recorded. One-hundred forty-four patients with MDR colonization were observed (6.5% of 2226 admissions). Extended spectrum beta-lactamase (ESBL)-producing (ESBL-P) enterobacteria were observed in 64/144 patients, carbapenem-resistant (CR) Gram-negative bacteria in 85/144 and vancomycin-resistant enterococci (VREs) in 9/144. Overall, 37 MDR-colonized patients (25.7%) developed at least one BSI; 23 of them (62.2%, 16% of the whole series) developed BSI by the same pathogen (MDRrel BSI), with a rate of 15.6% (10/64) for ESBL-P enterobacteria, 14.1% (12/85) for CR Gram-negative bacteria and 11.1% (1/9) for VRE. In 20/23 cases, MDRrel BSI occurred during neutropenia. After a median follow-up of 80\ua0days, 18 patients died (12.5%). The 3-month overall survival was significantly lower for patients colonized with CR Gram-negative bacteria (83.6%) and VRE (77.8%) in comparison with those colonized with ESBL-P enterobacteria (96.8%). CR-rel BSI and the presence of a urinary catheter were independent predictors of mortality. MDR rectal colonization occurs in 6.5% of haematological inpatients and predicts a 16% probability of MDRrel BSI, particularly during neutropenia, as well as a higher probability of unfavourable outcomes in CR-rel BSIs. Tailored empiric antibiotic treatment should be decided on the basis of colonization

COVID-19 infection in adult patients with hematological malignancies : a European Hematology Association Survey (EPICOVIDEHA)

Patients with hematological malignancies (HM) are at high risk of mortality from SARS-CoV-2 disease 2019 (COVID-19). A better understanding of risk factors for adverse outcomes may improve clinical management in these patients. We therefore studied baseline characteristics of HM patients developing COVID-19 and analyzed predictors of mortality. The survey was supported by the Scientific Working Group Infection in Hematology of the European Hematology Association (EHA). Eligible for the analysis were adult patients with HM and laboratory-confirmed COVID-19 observed between March and December 2020. The study sample includes 3801 cases, represented by lymphoproliferative (mainly non-Hodgkin lymphoma n = 1084, myeloma n = 684 and chronic lymphoid leukemia n = 474) and myeloproliferative malignancies (mainly acute myeloid leukemia n = 497 and myelodysplastic syndromes n = 279). Severe/critical COVID-19 was observed in 63.8% of patients (n = 2425). Overall, 2778 (73.1%) of the patients were hospitalized, 689 (18.1%) of whom were admitted to intensive care units (ICUs). Overall, 1185 patients (31.2%) died. The primary cause of death was COVID-19 in 688 patients (58.1%), HM in 173 patients (14.6%), and a combination of both COVID-19 and progressing HM in 155 patients (13.1%). Highest mortality was observed in acute myeloid leukemia (199/497, 40%) and myelodysplastic syndromes (118/279, 42.3%). The mortality rate significantly decreased between the first COVID-19 wave (March-May 2020) and the second wave (October-December 2020) (581/1427, 40.7% vs. 439/1773, 24.8%, p value < 0.0001). In the multivariable analysis, age, active malignancy, chronic cardiac disease, liver disease, renal impairment, smoking history, and ICU stay correlated with mortality. Acute myeloid leukemia was a higher mortality risk than lymphoproliferative diseases. This survey confirms that COVID-19 patients with HM are at high risk of lethal complications. However, improved COVID-19 prevention has reduced mortality despite an increase in the number of reported cases

Incidence, Risk Factors and Outcome of Pre-engraftment Gram-Negative Bacteremia after Allogeneic and Autologous Hematopoietic Stem Cell Transplantation: An Italian Prospective Multicenter Survey

Abstract BACKGROUND: Gram-negative bacteremia (GNB) is a major cause of illness and death after hematopoietic stem cell transplantation (HSCT), and updated epidemiological investigation is advisable. METHODS: We prospectively evaluated the epidemiology of pre-engraftment GNB in 1118 allogeneic HSCTs (allo-HSCTs) and 1625 autologous HSCTs (auto-HSCTs) among 54 transplant centers during 2014 (SIGNB-GITMO-AMCLI study). Using logistic regression methods. we identified risk factors for GNB and evaluated the impact of GNB on the 4-month overall-survival after transplant. RESULTS: The cumulative incidence of pre-engraftment GNB was 17.3% in allo-HSCT and 9% in auto-HSCT. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most common isolates. By multivariate analysis, variables associated with GNB were a diagnosis of acute leukemia, a transplant from a HLA-mismatched donor and from cord blood, older age, and duration of severe neutropenia in allo-HSCT, and a diagnosis of lymphoma, older age, and no antibacterial prophylaxis in auto-HSCT. A pretransplant infection by a resistant pathogen was significantly associated with an increased risk of posttransplant infection by the same microorganism in allo-HSCT. Colonization by resistant gram-negative bacteria was significantly associated with an increased rate of infection by the same pathogen in both transplant procedures. GNB was independently associated with increased mortality at 4 months both in allo-HSCT (hazard ratio, 2.13; 95% confidence interval, 1.45-3.13; P <.001) and auto-HSCT (2.43; 1.22-4.84; P = .01). CONCLUSIONS: Pre-engraftment GNB is an independent factor associated with increased mortality rate at 4 months after auto-HSCT and allo-HSCT. Previous infectious history and colonization monitoring represent major indicators of GNB. CLINICAL TRIALS REGISTRATION: NCT02088840

COVID-19 infection in adult patients with hematological malignancies: a European Hematology Association Survey (EPICOVIDEHA)

Background: Patients with hematological malignancies (HM) are at high risk of mortality from SARS-CoV-2 disease 2019 (COVID-19). A better understanding of risk factors for adverse outcomes may improve clinical management in these patients. We therefore studied baseline characteristics of HM patients developing COVID-19 and analyzed predictors of mortality. Methods: The survey was supported by the Scientifc Working Group Infection in Hematology of the European Hematology Association (EHA). Eligible for the analysis were adult patients with HM and laboratory-confrmed COVID19 observed between March and December 2020. Results: The study sample includes 3801 cases, represented by lymphoproliferative (mainly non- Hodgkin lymphoma n=1084, myeloma n=684 and chronic lymphoid leukemia n=474) and myeloproliferative malignancies (mainly acute myeloid leukemia n=497 and myelodysplastic syndromes n=279). Severe/critical COVID-19 was observed in 63.8% of patients (n=2425). Overall, 2778 (73.1%) of the patients were hospitalized, 689 (18.1%) of whom were admitted to intensive care units (ICUs). Overall, 1185 patients (31.2%) died. The primary cause of death was COVID19 in 688 patients (58.1%), HM in 173 patients (14.6%), and a combination of both COVID-19 and progressing HM in 155 patients (13.1%). Highest mortality was observed in acute myeloid leukemia (199/497, 40%) and myelodysplastic syndromes (118/279, 42.3%). The mortality rate signifcantly decreased between the frst COVID-19 wave (March–May 2020) and the second wave (October–December 2020) (581/1427, 40.7% vs. 439/1773, 24.8%, p value<0.0001). In the multivariable analysis, age, active malignancy, chronic cardiac disease, liver disease, renal impairment, smoking history, and ICU stay correlated with mortality. Acute myeloid leukemia was a higher mortality risk than lymphoproliferative diseases. Conclusions: This survey confrms that COVID-19 patients with HM are at high risk of lethal complications. However, improved COVID-19 prevention has reduced mortality despite an increase in the number of reported cases