Green compressive sampling reconstruction in IoT networks

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks

Voriconazole treatment of Candida tropicalis meningitis: persistence of (1,3)-b-D-glucan in the cerebrospinal fluid is a marker of clinical and microbiological failure

Introduction: Infections are still the most common complications of cerebral shunt procedures. Even though fungal etiologies are considered to be rare, they are associated with significant morbidity and mortality. Due to their uncommonness, diagnostic procedures and optimal therapy are poorly defined. We report a case of Candida tropicalis infection of ventriculo-peritoneal cerebrospinal fluid (CSF) shunt in a 49-year-old immune competent male treated with voriconazole (VOR). Methods: Microbiological and CSF markers (1,3-b-D-glucan-BDG) of fungal infection, biofilm production capacity, sensitivity of serial isolates of the pathogen, and the concentration of the antifungal drug have been monitored and related to the clinical course of this infection. Results: Despite appropriate treatment with VOR, in terms of adequate achieved CSF drug concentrations and initial effective therapeutic response, loss of VOR susceptibility of the C tropicalis and treatment failure were observed. Conclusion: Biofilm production of the C. tropicalis isolate might have had a significant role in treatment failure. Of interest, clinical and microbiological unfavorable outcome was anticipated by persistence of BDG in CSF. Rising titers of this marker were associated with relapse of fungal infection

Drug repositioning: auranofin as a prospective antimicrobial agent for the treatment of severe staphylococcal infections

Auranofin, (AF), a gold(I) complex in clinical use for the therapy of rheumatoid arthritis, is reported here to produce remarkable bactericidal effects in vitro against Staphylococcus sp. Noticeably, a similar antimicrobial action and potency are also noticed toward a few methicillin-resistant Staphylococcus aureus strains but not toward Escherichia coli. The time and concentration dependencies of the antimicrobial actions of AF have been characterized through recording time kill curves, and a concentration dependent profile highlighted. Overall, the present results point out that auranofin might be quickly and successfully repurposed for the treatment of severe bacterial infections due to resistant Staphylococci

Pharmacokinetic study of gemcitabine, given as prolonged infusion at fixed dose rate, in combination with cisplatin in patients with advanced non-small-cell lung cancer.

International audienc

Prospective phase II single-center study of the safety of a single very high dose of liposomal amphotericin B for antifungal prophylaxis in patients with acute myeloid leukemia.

Some preclinical and pharmacokinetic studies suggested the variable safety and the potential efficacy of an antifungal prophylaxis with a single high dose of liposomal amphotericin B (L-AmB) in high-risk patients. An open-label, prospective study was conducted with 48 adults receiving induction chemotherapy for acute myeloid leukemia (AML). Patients received a single infusion of 15 mg/kg of body weight L-AmB and, eventually, a second dose after 15 days of persistent neutropenia. The primary objective was tolerability and safety. Efficacy was also evaluated as a secondary endpoint. A pharmacokinetic study was performed with 34 patients in order to evaluate any association of plasma L-AmB levels with toxicity and efficacy. Overall, only 6 patients (12.5%) reported Common Toxicity Criteria (CTC) grade 3 hypokalemia, which was corrected with potassium supplementation in all cases, and no patient developed clinically relevant nephrotoxicity. Mild infusion-related adverse events occurred after 6 of 53 (11.3%) total infusions, with permanent drug discontinuation in only one case. Proven invasive fungal disease (IFD) was diagnosed in 4 (8.3%) patients. The mean AmB plasma levels at 6 h, 24 h, and 7 days after L-AmB administration were 160, 49.5, and 1 mg/liter, respectively. The plasma AmB levels were higher than the mean values of the overall population in 3 patients who developed CTC grade 3 hypokalemia and did not significantly differ from the mean values of the overall population in 3 patients who developed IFD. Our experience demonstrates the feasibility and safety of a single 15-mg/kg L-AmB dose as antifungal prophylaxis in AML patients undergoing induction chemotherapy

Improving J-Divergence of Brain Connectivity States by Graph Laplacian Denoising

International audienceFunctional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this work, we address the problem of denoising common connectivity estimates to improve the detectability of different connectivity states. Specifically, we propose a graph signal processing based denoising algorithm that acts on the network graph Laplacian. Further, we derive a novel formulation of the Jensen divergence for the denoised Laplacian under different states. Numerical simulations on synthetic data show that denoising improves the Jensen divergence of connectivity patterns corresponding to different task conditions. Furthermore, we apply the Laplacian denoising technique to brain networks estimated from real EEG data recorded during MI-BCI experiments. A novel formulation of the J-divergence allows to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states. Experimental results on real MI-BCI EEG data demonstrate that the Laplacian denoising improves the separation of motor imagery and resting mental states, and it shortens the time interval required for connectivity estimation. We conclude that the approach shows promise for robust detection of connectivity states while being appealing for implementation in real-time BCI applications

Management of meningitis caused by multi drug-resistant Acinetobacter baumannii: Clinical, microbiological and pharmacokinetic results in a patient treated with colistin methanesulfonate

This paper reports on a 71- year-old Caucasian male who underwent neurosurgery for an oligodendroglioma, followed by a cranial-sinus fistula and cerebrospinal fluid rhinorrhea. The clinical course was complicated due to an extensively drug-resistant Acinetobacter baumannii meningitis. The patient was treated with colistin methanesulfonate, intrathecal for 24 days and intravenous for 46 days. In addition, the patient received meropenem and teicoplanin to treat a urinary tract infection and a bacterial aspiration pneumonia. Cerebrospinal fluid trough colistin levels resulted above the MIC of A. baumannii. Colistin cerebrospinal fluid concentration did not increase over the treatment period. Meningitis was cured and A. baumannii eradicated. No side effects from the antimicrobial therapy were observed. In conclusion, this case highlights the issues in treating infections caused by resistant Gram negative bacteria and supports previous findings on the efficacy, pharmacokinetic and tolerability of intravenous and intrathecal colistin treatments

A Joint Markov Model for Communities, Connectivity and Signals Defined Over Graphs

Real-world networks are typically described in terms of nodes, links, and communities, having signal values often associated with them. The aim of this letter is to introduce a novel Compound Markov random field model (Compound MRF, or CMRF) for signals defined over graphs, encompassing jointly signal values at nodes, edge weights, and community labels. The proposed CMRF generalizes Markovian models previously proposed in the literature, since it accounts for different kinds of interactions between communities and signal smoothness constraints. Finally, the proposed approach is applied to (joint) graph learning and signal recovery. Numerical results on synthetic and real data illustrate the competitive performance of our method with respect to other state-of-the-art approaches