Chromodynamical analysis of lenticular galaxies using globular clusters and planetary nebulae

Recovering the origins of lenticular galaxies can shed light on the understanding of galaxy formation and evolution, since they present properties that can be found in both elliptical and spiral galaxies. In this work we study the kinematics of the globular cluster (GC) systems of three lenticular galaxies located in low density environments (NGC2768, NGC3115 and NGC7457), and compare them with the kinematics of planetary nebulae (PNe). The PNe and GC data come from the Planetary Nebulae Spectrograph and the SLUGGS Surveys. Through photometric spheroid-disc decomposition and PNe kinematics we find the probability for a given GC to belong to either the spheroid or the disc of its host galaxy or be rejected from the model. We find that there is no correlation between the components that the GCs are likely to belong to and their colours. Particularly, for NGC2768 we find that its red GCs display rotation preferentially at inner radii (Re < 1). In the case of the GC system of NGC3115 we find a group of GCs with similar kinematics that are not likely to belong to neither its spheroid nor disc. For NGC7457 we find that 70% of its GCs are likely to belong to the disc. Overall, our results suggest that these galaxies assembled into S0s through different evolutionary paths. Mergers seem to have been very important for NGC2768 and NGC3115 while NGC7457 is more likely to have experienced secular evolution

The impact of COVID-19 measures on parent-reported restricted and repetitive behaviours in pre-school children with autism

Background: COVID-19 restrictive measures have had a considerable impact on daily life routines, which may be especially challenging for families of children with autism. In pre-schoolers with autism, it is likely that the disruption of routines mainly impacts the presence of restrictive and repetitive behaviours (RRBs). Furthermore, influence of comorbid conditions, secondary behavioural difficulties and home environment characteristics on RRBs was explored.Method: A cross-sectional online survey design was used to collect parent-report data on 254 children with autism (2.5-6 years) during lockdown in the early months of the pandemic. RRBs were assessed using the Repetitive Behaviour Scale-Revised (RBS-R).Results: Parents reported a significant increase in stereotypic, self-injurious, compulsive and ritualistic behaviour, and restricted interests after implementation of COVID-19 restrictions. The presence of a co-occurring condition, such as language impairments or intellectual disability, was associated with more self-injurious and stereotypic behaviour. However, there was no effect of home environment on RRBs. Further, most children showed increases in internalising and/or externalising behaviour. Increased inattentive behaviour was associated with more ritu-alistic and stereotypic behaviour, and restricted interests. Decreases in hyperactivity were related to more restricted interests. Importantly, in a subset of children, parents reported less behavioural difficulties during the lockdown.Conclusions: Findings highlight the importance of flexible implementation and continuity of care for pre-schoolers with autism and support for parents. Further follow-up of children with autism and RRBs, and co-occurring behavioural difficulties is needed and could enhance our understanding of the long-term effects associated with sudden restrictive measures to daily routines

Implicit cognitive flexibility in self-limited focal epilepsy of childhood: An HD-EEG study

International audienc

Chromodynamical analysis of lenticular galaxies using globular clusters and planetary nebulae

Recovering the origins of lenticular galaxies can shed light on the understanding of galaxy formation and evolution, since they present properties that can be found in both elliptical and spiral galaxies. In this work we study the kinematics of the globular cluster (GC) systems of three lenticular galaxies located in low density environments (NGC2768, NGC3115 and NGC7457), and compare them with the kinematics of planetary nebulae (PNe). The PNe and GC data come from the Planetary Nebulae Spectrograph and the SLUGGS Surveys. Through photometric spheroid-disc decomposition and PNe kinematics we find the probability for a given GC to belong to either the spheroid or the disc of its host galaxy or be rejected from the model. We find that there is no correlation between the components that the GCs are likely to belong to and their colours. Particularly, for NGC2768 we find that its red GCs display rotation preferentially at inner radii (Re < 1). In the case of the GC system of NGC3115 we find a group of GCs with similar kinematics that are not likely to belong to neither its spheroid nor disc. For NGC7457 we find that 70% of its GCs are likely to belong to the disc. Overall, our results suggest that these galaxies assembled into S0s through different evolutionary paths. Mergers seem to have been very important for NGC2768 and NGC3115 while NGC7457 is more likely to have experienced secular evolution

Impact of the Environment on the PNIPAM Dynamical Transition Probed by Elastic Neutron Scattering

By means of elastic incoherent neutron scattering, we investigated how the addition of stabilizing cosolvents (glycerol and glucose) affects the dynamics of hydrated PNIPAM chains at the pico- and nanosecond time scale, where a low-temperature dynamical transition is observed. From the elastic intensities, the atomic mean square displacements of the PNIPAM samples were extracted using a global fitting procedure. Both the dynamical transition temperature Td and the amplitude of the displacements are found to be strongly dependent on solvent composition. The close analogies between the dynamical transition of PNIPAM and that of biomolecules reveal PNIPAM as an excellent system for reproducing complex solvent−biopolymer interactions

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Background: Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results: Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered model