Impact of sleep disorders on behavioral issues in preschoolers with autism spectrum disorder

BackgroundSleep disorders are one of the most common problems in children with Autism Spectrum Disorder (ASD). However, they often tend to be underdiagnosed and incorrectly treated in clinical practice. This study aims to identify sleep disorders in preschool children with ASD and to explore their relationship with the core symptoms of autism, the child's developmental and cognitive level as well as the psychiatric comorbidities. MethodsWe recruited 163 preschool children with a diagnosis of ASD. The Children's Sleep Habits Questionnaire (CSHQ) assessed sleep conditions. Multiple standardized tests were used to evaluate intellectual abilities, the presence of repetitive behaviors (through the Repetitive Behavior Scale-Revised), as well as the emotional-behavioral problems and the psychiatric comorbidities (through the Child Behavior Checklist -CBCL 1(1/2)-5). ResultsThe results showed that poor disorders had consistently higher scores in all areas assessed by the CSHQ and on the CBCL across all domains. The correlational analysis showed that severe sleep disorders were associated with higher scores in internalizing, externalizing, and total problems at the CBCL syndromic scales, and in all DSM-oriented CBCL subscales. Moreover, we found that the association between sleep disorders and restricted and repetitive behaviors (RRBs) is explained by the anxiety-related symptoms. ConclusionBased on these findings, the study recommends that screening for sleep problems followed by early intervention should constitute a routine part of clinical practice for children with ASD

Metronomic oral cyclophosphamide (MOC) in the salvage therapy of heavily treated recurrent ovarian cancer patients: a retrospective, multicenter study

The aim of this multicenter, retrospective study was to evaluate the efficacy and safety of metronomic oral cyclophosphamide (MOC) in heavily treated, relapsed ovarian cancer (ROC) patients

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

OltreMare - Un progetto per il futuro della Biodiversità del Mediterraneo

Osservatorio e comunicazione. Questo progetto narra dello sguardo degli artisti dell’Accademia di Belle Arti di Palermo sul lavoro di ricerca portato avanti dall’IAS - CNR (ex IAMC) riguardo all’osservazione e alla tutela della Biodiversità e costituisce uno strumento eccellente di comunicazione per un pubblico quanto mai ampio. La divulgazione della scienza è un’attività complessa e sicuramente necessita di competenze e attitudini multidisciplinari oltreché di motivazione ed entusiasmo. La comunicazione delle tematiche scientifiche, di per sè ostiche nella traduzione al grande pubblico, grazie alla forza e all’immediatezza tipica dell’espressione artistica diventa prodigioso spunto di riflessione e di osservazione, sia per i giovani che per la comunità intera. Grazie al progetto Osservatorio della Biodiversità Siciliana, sono state realizzate da partners con competenze istituzionali complementari , quali l’Accademia di Belle Arti di Palermo e l’IAS - CNR di Capo Granitola, delle azioni didattiche e creative di valore scientifico espresse con straordinaria forza e bellezza. La sinergia creata, nata da un rapporto consolidato ormai da tempo, ha portato ad uno scambio tra ricercatori e professori che si sono messi in gioco in uno sforzo congiunto per avvicinare le proprie competenze. In seguito ad un’intensa attività di coordinamento e pianificazione dei lavori, si è riusciti a portare avanti un progetto ambizioso e imponente, coinvolgendo moltissimi ambiti scientifici e altrettante cattedre, sensibilizzando così gli artisti ai temi della Biodiversità. Le opere prodotte, accompagnate da schede scientifiche, hanno dunque acquisito un valore, oltreché artistico, didattico, e restano come testimonianze oggettive, nel percorso culturale, per i visitatori dell’Osservatorio. Questa collaborazione conferma l’importanza e l’opportunità di unire arte e scienza per esaltare la percezione della ricerca scientifica da parte della comunità e ,ancora una volta, si conferma come, per fare “cose straordinarie”, siano più importanti i rapporti umani piuttosto che le competenze tecniche. A tal proposito, un ringraziamento sentito al Prof. Calogero Piro che, con passione e dedizione, ha reso possibile questa esperienza, e al gruppo di Comunicazione EDU Lab dell’IAS - CNR, che è stato, per me, un supporto indispensabile per la realizzazione di questo complesso progetto

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches