10 research outputs found
Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability
The amount of information exchanged per unit of time between two dynamic processes is
an important concept for the analysis of complex systems. Theoretical formulations and
data-efficient estimators have been recently introduced for this quantity, known as the
mutual information rate (MIR), allowing its continuous-time computation for event-based
data sets measured as realizations of coupled point processes. This work presents the
implementation of MIR for point process applications in Network Physiology and
cardiovascular variability, which typically feature short and noisy experimental time
series. We assess the bias of MIR estimated for uncoupled point processes in the
frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR)
measure designed to return zero values when the two processes do not exchange
information. The method is first tested extensively in synthetic point processes
including a physiologically-based model of the heartbeat dynamics and the blood
pressure propagation times, where we show the ability of cMIR to compensate the
negative bias of MIR and return statistically significant values even for weakly coupled
processes. The method is then assessed in real point-process data measured from
healthy subjects during different physiological conditions, showing that cMIR between
heartbeat and pressure propagation times increases significantly during postural stress,
though not during mental stress. These results document that cMIR reflects physiological
mechanisms of cardiovascular variability related to the joint neural autonomic modulation
of heart rate and arterial compliance
Gamma-Ray Burst observations by the high-energy charged particle detector on board the CSES-01 satellite between 2019 and 2021
In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs)
by the High-Energy Particle Detector (HEPD-01) mounted on board the China
Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a
Sun-synchronous polar orbit at a 507 km altitude and 97
inclination. HEPD-01 was designed to detect high-energy electrons in the energy
range 3 - 100 MeV, protons in the range 30 - 300 MeV, and light nuclei in the
range 30 - 300 MeV/n. Nonetheless, Monte Carlo simulations have shown HEPD-01
is sensitive to gamma-ray photons in the energy range 300 keV - 50 MeV, even if
with a moderate effective area above 5 MeV. A dedicated time correlation
analysis between GRBs reported in literature and signals from a set of HEPD-01
trigger configuration masks has confirmed the anticipated detector sensitivity
to high-energy photons. A comparison between the simultaneous time profiles of
HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A,
GRB200826B and GRB211211A has shown a remarkable similarity, in spite of the
different energy ranges. The high-energy response, with peak sensitivity at
about 2 MeV, and moderate effective area of the detector in the actual flight
configuration explain why these five GRBs, characterised by a fluence above
3 10 erg cm in the energy interval 300 keV - 50
MeV, have been detected.Comment: Accepted for publication in The Astrophysical Journal (ApJ
Estimating Permutation Entropy Variability via Surrogate Time Series
In the last decade permutation entropy (PE) has become a popular tool to analyze the degree of randomness within a time series. In typical applications, changes in the dynamics of a source are inferred by observing changes of PE computed on different time series generated by that source. However, most works neglect the crucial question related to the statistical significance of these changes. The main reason probably lies in the difficulty of assessing, out of a single time series, not only the PE value, but also its uncertainty. In this paper we propose a method to overcome this issue by using generation of surrogate time series. The analysis conducted on both synthetic and experimental time series shows the reliability of the approach, which can be promptly implemented by means of widely available numerical tools. The method is computationally affordable for a broad range of users
Motibot - The Virtual Coach for Healthy Coping Intervention among adults with diabetes: A Proof-of-Concept study
none6noBackground: Motivation is a core component of diabetes self-management because it allows adults with diabetes mellitus (DM)
to adhere to clinical recommendations. In this context, virtual coaches (VCs) have assumed a central role in supporting and
treating common barriers related to adherence. However, most of them are mainly focused on medical and physical purposes,
such as the monitoring of blood glucose levels or following a healthy diet.
Objective: This proof-of-concept study aims to evaluate the preliminary efficacy of a VC intervention for psychosocial support
before and after the intervention and at follow-up. The intent of this VC is to motivate adults with type 1 DM and type 2 DM to
adopt and cultivate healthy coping strategies to reduce symptoms of depression, anxiety, perceived stress, and diabetes-related
emotional distress, while also improving their well-being.
Methods: A total of 13 Italian adults with DM (18-51 years) interacted with a VC, called Motibot (motivational bot) using the
Telegram messaging app. The interaction covered 12 sessions, each lasting 10 to 20 minutes, during which the user could dialogue
with the VC by inputting text or tapping an option on their smartphone screen. Motibot is developed within the transtheoretical
model of change to deliver the most appropriate psychoeducational intervention based on the user’s motivation to change.
Results: Results showed that over the 12 sessions, there were no significant changes before and after the intervention and at
follow-up regarding psychosocial factors. However, most users showed a downward trend over the 3 time periods in depression
and anxiety symptoms, thereby presenting good psychological well-being and no diabetes-related emotional distress. In addition,
users felt motivated, involved, encouraged, emotionally understood, and stimulated by Motibot during the interaction. Indeed,
the analyses of semistructured interviews, using a text mining approach, showed that most users reported a perceived reduction
in anxiety, depression, and/or stress symptoms. Moreover, users indicated the usefulness of Motibot in supporting and motivating
them to find a mindful moment for themselves and to reflect on their own emotions.
Conclusions: Motibot was well accepted by users, particularly because of the inclusion of mindfulness practices, which motivated
them to adopt healthy coping skills. To this extent, Motibot provided psychosocial support for adults with DM, particularly for
those with mild and moderate symptoms, whereas those with severe symptoms may benefit more from face-to-face psychotherapy.noneGiulia Bassi; Claudio Giuliano; Alessio Perinelli; Stefano Forti; Silvia Gabrielli; Silvia SalcuniBassi, Giulia; Giuliano, Claudio; Perinelli, Alessio; Forti, Stefano; Gabrielli, Silvia; Salcuni, Silvi
Evidence of an upper ionospheric electric field perturbation correlated with a gamma ray burst
Abstract Earth’s atmosphere, whose ionization stability plays a fundamental role for the evolution and endurance of life, is exposed to the effect of cosmic explosions producing high energy Gamma-ray-bursts. Being able to abruptly increase the atmospheric ionization, they might deplete stratospheric ozone on a global scale. During the last decades, an average of more than one Gamma-ray-burst per day were recorded. Nevertheless, measurable effects on the ionosphere were rarely observed, in any case on its bottom-side (from about 60 km up to about 350 km of altitude). Here, we report evidence of an intense top-side (about 500 km) ionospheric perturbation induced by significant sudden ionospheric disturbance, and a large variation of the ionospheric electric field at 500 km, which are both correlated with the October 9, 2022 Gamma-ray-burst (GRB221009A)