Coexistence of amplitude and frequency modulations in intracellular calcium dynamics

The complex dynamics of intracellular calcium regulates cellular responses to information encoded in extracellular signals. Here, we study the encoding of these external signals in the context of the Li-Rinzel model. We show that by control of biophysical parameters the information can be encoded in amplitude modulation, frequency modulation or mixed (AM and FM) modulation. We briefly discuss the possible implications of this new role of information encoding for astrocytes.Comment: 4 pages, 4 figure

A sensing seat for human authentication

Abstract-This work is focused on the design and the realization of a sensing seat system for human authentication. Such a system may be used for security purposes in trucks, cars, offices, and scenarios where human subject authentication is needed and a seat is available. The sensing seat is realized by a seat coated with a removable Lycra sensing cover equipped with a piezoresistive sensor network. Since each sensor consists of a conductive elastomer composite rubber screen printed onto a cotton Lycra fabric, the sensing cover is able to respond to simultaneous deformations in different areas. This technology avoids the use of rigid electronic components and enables the realization of different cover layouts according to different types of seats. The algorithms for the enrollment, authentication, and monitoring tasks are discussed. A measurement campaign was carried out using data from 40 human subjects. The authentication capabilities of the system are reported in terms of acceptance and rejection rates, showing a high degree of correct classification

Autologous Haematopoietic Stem Cell Transplantation and Systemic Sclerosis: Focus on Interstitial Lung Disease

Autologous hematopoietic stem cells transplantation (AHSCT) has been employed as treatment for severe systemic sclerosis (SSc) with high risk of organ failure. In the last 25 years overall survival and treatment-related mortality have improved, in accordance with a better patient selection and mobilization and conditioning protocols. This review analyzes the evidence from the last 5 years for AHSCT-treated SSc patients, considering in particular the outcomes related to interstitial lung disease. There are increasing data supporting the use of AHSCT in selected patients with rapidly progressive SSc. However, some unmet needs remain, such as an accurate patient selection, pre-transplantation analysis to identify subclinical conditions precluding the transplantation, and the alternatives for post-transplant ILD recurrence

An Ecological Visual Exploration Tool to Support the Analysis of Visual Processing Pathways in Children with Autism Spectrum Disorders

Recent improvements in the field of assistive technologies have led to innovative solutions aiming at increasing the capabilities of people with disability, helping them in daily activities with applications that span from cognitive impairments to developmental disabilities. In particular, in the case of Autism Spectrum Disorder (ASD), the need to obtain active feedback in order to extract subsequently meaningful data becomes of fundamental importance. In this work, a study about the possibility of understanding the visual exploration in children with ASD is presented. In order to obtain an automatic evaluation, an algorithm for free (i.e., without constraints, nor using additional hardware, infrared (IR) light sources or other intrusive methods) gaze estimation is employed. Furthermore, no initial calibration is required. It allows the user to freely rotate the head in the field of view of the sensor, and it is insensitive to the presence of eyeglasses, hats or particular hairstyles. These relaxations of the constraints make this technique particularly suitable to be used in the critical context of autism, where the child is certainly not inclined to employ invasive devices, nor to collaborate during calibration procedures.The evaluation of children’s gaze trajectories through the proposed solution is presented for the purpose of an Early Start Denver Model (ESDM) program built on the child’s spontaneous interests and game choice delivered in a natural setting

Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening.

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM-recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings

Validation of the Quantitative Checklist for Autism in Toddlers in an Italian Clinical Sample of Young Children With Autism and Other Developmental Disorders.

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers. Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children. Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained. Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills. Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay

Disentangling the initiation from the response in joint attention: An eye-tracking study in toddlers with autism spectrum disorders

Joint attention (JA), whose deficit is an early risk marker for autism spectrum disorder (ASD), has two dimensions: (1) responding to JA and (2) initiating JA. Eye-tracking technology has largely been used to investigate responding JA, but rarely to study initiating JA especially in young children with ASD. The aim of this study was to describe the differences in the visual patterns of toddlers with ASD and those with typical development (TD) during both responding JA and initiating JA tasks. Eye-tracking technology was used to monitor the gaze of 17 children with ASD and 15 age-matched children with TD during the presentation of short video sequences involving one responding JA and two initiating JA tasks (initiating JA-1 and initiating JA-2). Gaze accuracy, transitions and fixations were analyzed. No differences were found in the responding JA task between children with ASD and those with TD, whereas, in the initiating JA tasks, different patterns of fixation and transitions were shown between the groups. These results suggest that children with ASD and those with TD show different visual patterns when they are expected to initiate joint attention but not when they respond to joint attention. We hypothesized that differences in transitions and fixations are linked to ASD impairments in visual disengagement from face, in global scanning of the scene and in the ability to anticipate object's action