Accurate hemodynamic response estimation by removal of stimulus-evoked superficial response in fNIRS signals

Objective. We address the problem of hemodynamic response (HR) estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the HR estimation; therefore, careful and accurate denoising of data is needed. Approach. We propose a dictionary-based algorithm to process each single event-related segment of the acquired signal for both long separation (LS) and short separation (SS) channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the SS channel is employed to correct the evoked superficial response and refine the HR estimate from the LS channel. Main results. Analysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for oxy-hemoglobin (HbO) and 78% for deoxy-hemoglobin (HbR), considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial HR estimation, a significant reduction in the number of averaged trials can also be obtained. Significance. This work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. We demonstrate the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based HR analysis

Validation of a sectional soot model based on a constant pressure tabulated chemistry approach for PM, PN and PSDF estimation in a GDI research engine

Findings from the International Agency for Research on Cancer (IARC) classified particulate matter (PM) as carcinogenic to humans. While being a promising solution to reduce greenhouse gases (GHG) emissions and increase engine fuel economy, Gasoline Direct Injected (GDI) engines produce a number of particles (PN) of fine size higher than Port Fuel Injected (PFI) ones. As a consequence, the EU commission significantly tightened the emission standards for passenger cars, following which all gasoline engines will have to meet the euro-6d regulation coming into force in 2020. Efforts are made by the research community to understand the root causes leading to soot formation and possibly identify technical solutions to lower it. An important piece of the puzzle is the investigation of soot formation via 3D-CFD. To this aim, relevant efforts have been and are still being paid to adapt soot emissions models, originally developed for Diesel combustion, for GDI units. Among the many available models, one of the most advanced is the so-called Sectional Method. So far, studies presented in literature were not able to formulate a methodology to quantitatively match experimental PM, PN and PSDF without a dedicated soot model tuning. In the present work, a Sectional Method-based methodology to quantitatively predict GDI soot is presented and validated against PM, PN and PSDF measurements on a optically accessible GDI research unit. While adapting the model to GDI soot, attention is devoted to the modelling of soot precursor chemistry: a customized version of a pre-existing chemical kinetics mechanism, used to predict the formation of the key PAH (Polycyclic Aromatic Hydrocarbons) species, is presented and validated via 1D numerical simulations on a premixed flat flame burner dataset available in literature. The present work demonstrates that a Sectional Method-based approach can be a powerful tool to quantitatively predict engine-out soot emissions

Effect of the microtubule-associated protein tau on dynamics of single-headed motor proteins KIF1A

Intracellular transport based on molecular motors and its regulation are crucial to the functioning of cells. Filamentary tracks of the cells are abundantly decorated with nonmotile microtubule-associated proteins, such as tau. Motivated by experiments on kinesin-tau interactions [Dixit et al., Science 319, 1086 (2008)] we developed a stochastic model of interacting single-headed motor proteins KIF1A that also takes into account the interactions between motor proteins and tau molecules. Our model reproduces experimental observations and predicts significant effects of tau on bound time and run length which suggest an important role of tau in regulation of kinesin-based transport.publishedVersionFil: Sparacino, Javier. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Sparacino, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Farias, María Gimena. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina.Fil: Farias, María Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina.Fil: Farias, María Gimena. Ministerio de Ciencia, Tecnología e Innovación. Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación. Fondo para la Investigación Científica y Tecnológica; Argentina.Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Otras Ciencias Física

Quantifying High-Order Interactions in Cardiovascular and Cerebrovascular Networks

We present a method to analyze the dynamics of physiological networks beyond the framework of pairwise interactions. Our method defines the so-called O-information rate (OIR) as a measure of the higher-order interaction among several physiological variables. The OIR measure is computed from the vector autoregressive representation of multiple time series, and is applied to the network formed by heart period, systolic and diastolic arterial pressure, respiration and cerebral blood flow variability series measured in healthy subjects at rest and after head-up tilt. Our results document that cardiovascular, cerebrovascular and respiratory interactions are highly redundant, and that redundancy is enhanced by the entrainment of cardiovascular and cerebrovascular oscillations and by sympathetic activation

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Assessment of Cardiorespiratory Interactions During Spontaneous and Controlled Breathing: Non-linear Model-free Analysis

In this work, nonlinear model-free methods for bivariate time series analysis have been applied to study cardiorespiratory interactions. Specifically, entropy-based (i.e. Transfer Entropy and Cross Entropy) and Convergent Cross Mapping asymmetric coupling measures have been computed on heart rate and breathing time series extracted from electrocardiographic (ECG) and respiratory signals acquired on 19 young healthy subjects during an experimental protocol including spontaneous and controlled breathing conditions. Results evidence a bidirectional nature of cardiorespiratory interactions, and highlight clear similarities and differences among the three considered measures

Shewanella algae infection in Italy: report of 3 years' evaluation along the coast of the northern Adriatic Sea

Shewanella algae are Gram-negative, nonfermentative, motile bacilli, classified in the genus Shewanella in 1985. These environmental bacteria are occasionally identified in human infections, with a relatively strong association with exposure to seawater during warm seasons. This report describes a case series of 17 patients with infection correlated to S. algae in the coastal area of Romagna, Italy, from 2013 to 2016. The types of infection included otitis, pneumonia, sepsis and soft tissue (wound). Exposure to the marine environment during hot months was confirmed in 12 of 17 patients. An apparent correlation between increased severity of infection and patient age was also observed

Orientamento dei Centri Trapianto Italiani in tema di HCV e Trapianto Renale

Abstract non disponibil

Determinants of enhanced thromboxane biosynthesis in renal transplantation

Background. Despite great improvement in patient and graft survival, the long-term morbidity and mortality in renal transplant recipients (RTRs) are still significant, with a high incidence of cardiovascular disease-related deaths. Methods. We investigated thromboxane (TXA2) biosynthesis and endothelial and coagulative activation in 65 patients who received a renal transplant. Results. The rate of TXA2 biosynthesis (urinary 11-dehydro-TXB2 excretion largely reflects platelet TXA2 production in vivo) was significantly (P < 0.0001) higher in RTRs than in healthy subjects. Plasma von Willebrand factor (vWF) and thrombin-antithrombin (TAT) complexes were significantly higher (P < 0.001) in RTRs compared with controls. Urinary 11-dehydro-TXB2 directly correlated with plasma vWF and cholesterol. We next examined the relative influence of cyclosporine A (CsA) on TXA2 biosynthesis and endothelial activation, comparing a group of RTRs not receiving CsA with an age- and sex-matched group of patients treated with CsA. Urinary excretion of 11-dehydro-TXB2 and plasma levels of vWF were significantly increased in RTRs who received CsA compared with those who did not. After an overall follow-up of 120 months, RTRs who experienced cardiovascular events had a higher frequency of abnormal plasma levels of vWF than patients who remained event free. Conclusion. Renal transplantation is associated with in vivo platelet activation highly related to endothelial activation. This is particularly evident in CsA-treated patients. Administration of drugs that are able to reduce or eliminate thromboxane-dependent platelet activation in vivo may be beneficial to reduce the risk of cardiovascular events in RTRs

Mycophenolate mofetil versus azathioprine for prevention of acute rejection in renal transplantation (MYSS): a randomised trial.

BACKGROUND: Mycophenolate mofetil has replaced azathioprine in immunosuppression regimens worldwide to prevent graft rejection. However, evidence that its antirejection activity is better than that of azathioprine has been provided only by registration trials with an old formulation of ciclosporin and steroid. We aimed to compare the antirejection activity of these two drugs with a new formulation of ciclosporin. METHODS: The mycophenolate steroids sparing multicentre, prospective, randomised, parallel-group trial compared acute rejections and adverse events in recipients of cadaver-kidney transplants over 6-month treatment with mycophenolate mofetil or azathioprine along with ciclosporin microemulsion (Neoral) and steroids (phase A), and over 15 more months without steroids (phase B). The primary endpoint was occurrence of acute rejection episodes. Analysis was by intention to treat. FINDINGS: 168 patients per group entered phase A. 56 (34%) assigned mycophenolate mofetil and 58 (35%) assigned azathioprine had clinical rejections (risk reduction [RR] on mycophenolate mofetil compared with azathioprine 13.7% [95% CI -25.7% to 40.7%], p=0.44). 88 patients in the mycophenolate mofetil group and 89 in the azathioprine group entered phase B. 14 (16%) taking mycophenolate mofetil and 11 (12%) taking azathioprine had clinical rejections (RR -16.2%, [-157.5% to 47.5%], p=0.71). Average per-patient costs of mycophenolate mofetil treatment greatly exceeded those of azathioprine (phase A 2665 Euros [SD 586] vs Euros 184 [62]; phase B 5095 Euros [2658] vs 322 Euros [170], p<0.0001 for both). INTERPRETATION: In recipients of cadaver kidney-transplants given ciclosporin microemulsion, mycophenolate mofetil offers no advantages over azathioprine in preventing acute rejections and is about 15 times more expensive. Standard immunosuppression regimens for transplantation should perhaps include azathioprine rather than mycophenolate mofetil, at least for kidney graft