comment on shore et al association between hyperglycemia at admission during hospitalization for acute myocardial infarction and subsequent diabetes insights from the veterans administration cardiac care follow up clinical study diabetes care 2014 37 409 418

We read with great interest the recently published article by Shore et al. (1) that addresses the important issue of admission hyperglycemia during hospitalization for acute myocardial infarction (AMI). The authors measured the prevalence of admission hyperglycemia in a very large cohort of AMI patients without known diabetes and examined its association with new evidence of diabetes in the 6 months following hospitalization. Diagnostic codes for diabetes, outpatient prescriptions for glucose-lowering medications, and/or HbA1c ≥6.5% during or after the index hospitalization were used for

Smart home energy management including renewable sources: A QoE-driven Approach

Smart Home Energy Management (SHEM) systems can introduce adjustments in the working period and operations of the home appliances to allow for energy cost savings, which can however affect the Quality of Experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoE-aware SHEM system, which relies on the knowledge of the annoyance suffered by the users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles which describe different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware Cost Saving Appliance Scheduling and the QoEaware Renewable Source Power Allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices making use of a greedy approach. The latter re-allocates appliances' operations whenever a surplus of energy has been made available by renewable energy sources. Experimental results demonstrate that the annoyance perceived by the users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving

The Prostacyclin Analogue Iloprost as an Early Predictor of Successful Revascularization in Diabetic Patients Affected by Critical Limb Ischemia and Foot Ulcers

Abstract Purpose The aim of this study is to evaluate the role of Iloprost as an early predictor of successful revascularization in patients affected by ischemic diabetic foot ulcers (DFUs). Methods Consecutive patients with ischemic DFUs with persistent low TcPO2 ( All patients underwent Iloprost infusion and TcPO2 has been recorded at days 3, 14 and 30. According to the TcPO2 reported at day 3, patients were divided into two groups: group A (patients with TcPO2 ≥30mmHg) and group B (patients with TcPO2  Results Twenty-five patients have been included, 12/25 (48%) in Group A and 13/25 (52%) in Group B. There were no significant differences at the baseline and one day after PTA between the two groups while TcPO2 values recorded in Group A at days 3, 14 and 30 after Iloprost infusion were significant higher in comparison to the Group B (χ = 0.005). The rate of re-PTA were respectively 33,3% (Group A) and 53,8% (Group B) (p = 0.03). Conclusions Iloprost may be an early predictor of successful revascularization in patients affected by critical limb ischemia (CLI) and DFUs

ALOHA: A Unified Platform-Aware Evaluation Method for CNNs Execution on Heterogeneous Systems at the Edge

CNN design and deployment on embedded edge-processing systems is an error-prone and effort-hungry process, that poses the need for accurate and effective automated assisting tools. In such tools, pre-evaluating the platform-aware CNN metrics such as latency, energy cost, and throughput is a key requirement for successfully reaching the implementation goals imposed by use-case constraints. Especially when more complex parallel and heterogeneous computing platforms are considered, currently utilized estimation methods are inaccurate or require a lot of characterization experiments and efforts. In this paper, we propose an alternative method, designed to be flexible, easy to use, and accurate at the same time. Considering a modular platform and execution model that adequately describes the details of the platform and the scheduling of different CNN operators on different platform processing elements, our method captures precisely operations and data transfers and their deployment on computing and communication resources, significantly improving the evaluation accuracy. We have tested our method on more than 2000 CNN layers, targeting an FPGA-based accelerator and a GPU platform as reference example architectures. Results have shown that our evaluation method increases the estimation precision by up to 5× for execution time, and by 2\times for energy, compared to other widely used analytical methods. Moreover, we assessed the impact of the improved platform-awareness on a set of neural architecture search experiments, targeting both hardware platforms, and enforcing 2 sets of latency constraints, performing 5 trials on each search space, for a total number of 20 experiments. The predictability is improved by 4\times , reaching, with respect to alternatives, selection results clearly more similar to those obtained with on-hardware measurements

Modelling and Automated Implementation of Optimal Power Saving Strategies in Coarse-Grained Reconfigurable Architectures

This paper focuses on how to efficiently reduce power consumption in coarse-grained reconfigurable designs, to allow their effective adoption in heterogeneous architectures supporting and accelerating complex and highly variable multifunctional applications. We propose a design flow for this kind of architectures that, besides their automatic customization, is also capable of determining their optimal power management support. Power and clock gating implementation costs are estimated in advance, before their physical implementation, on the basis of the functional, technological, and architectural parameters of the baseline design. Experimental results, on 90 and 45 nm CMOS technologies, demonstrate that the proposed approach guides the designer towards optimal implementation

Prognostic impact of coronary microcirculation abnormalities in systemic sclerosis: a prospective study to evaluate the role of non-invasive tests

INTRODUCTION: Microcirculation dysfunction is a typical feature of systemic sclerosis (SSc) and represents the earliest abnormality of primary myocardial involvement. We assessed coronary microcirculation status by combining two functional tests in SSc patients and estimating its impact on disease outcome. METHODS: Forty-one SSc patients, asymptomatic for coronary artery disease, were tested for coronary flow velocity reserve (CFR) by transthoracic-echo-Doppler with adenosine infusion (A-TTE) and for left ventricular wall motion abnormalities (WMA) by dobutamine stress echocardiography (DSE). Myocardial multi-detector computed tomography (MDCT) enabled the presence of epicardial stenosis, which could interfere with the accuracy of the tests, to be excluded. Patient survival rate was assessed over a 6.7- ± 3.5-year follow-up. RESULTS: Nineteen out of 41 (46%) SSc patients had a reduced CFR (≤2.5) and in 16/41 (39%) a WMA was observed during DSE. Furthermore, 13/41 (32%) patients showed pathological CFR and WMA. An inverse correlation between wall motion score index (WMSI) during DSE and CFR value (r = -0.57, P <0.0001) was observed; in addition, CFR was significantly reduced (2.21 ± 0.38) in patients with WMA as compared to those without (2.94 ± 0.60) (P <0.0001). In 12 patients with abnormal DSE, MDCT was used to exclude macrovasculopathy. During a 6.7- ± 3.5-year follow-up seven patients with abnormal coronary functional tests died of disease-related causes, compared to only one patient with normal tests. CONCLUSIONS: A-TTE and DSE tests are useful tools to detect non-invasively pre-clinical microcirculation abnormalities in SSc patients; moreover, abnormal CFR and WMA might be related to a worse disease outcome suggesting a prognostic value of these tests, similar to other myocardial diseases

PMU-based distribution system state estimation with adaptive accuracy exploiting local decision metrics and IoT paradigm

A novel adaptive distribution system state estimation (DSSE) solution is presented and discussed, which relies on distributed decision points and exploits the Cloud-based Internet of Things (IoT) paradigm. Up to now, DSSE procedures have been using fixed settings regardless of the actual values of measurement accuracy, which is instead affected by the actual operating conditions of the network. The proposed DSSE is innovative with respect to previous literature, because it is adaptive in the use of updated accuracies for the measurement devices. The information used in the estimation process along with the rate of the execution are updated, depending on the indications of appropriate local metrics aimed at detecting possible variations in the operating conditions of the distribution network. Specifically, the variations and the trend of variation of the rms voltage values obtained by phasor measurement units (PMUs) are used to trigger changes in the DSSE. In case dynamics are detected, the measurement data are sent to the DSSE at higher rates and the estimation process runs consequently, updating the accuracy values to be considered in the estimation. The proposed system relies on a Cloud-based IoT platform, which has been designed to incorporate heterogeneous measurement devices, such as PMUs and smart meters. The results obtained on a 13-bus system demonstrate the validity of the proposed methodology that is efficient both in the estimation process and in the use of the communication resources