It's SNARC o' clock: manipulating the salience of the context in a conceptual replication of Bächtold et al.'s (1998) clockface study.

The Spatial-Numerical Association of Response Codes (SNARC) effect consists in faster left-/right-key responses to small/large numbers. (Bächtold et al., Neuropsychologia 36:731-735, 1998) reported the reversal of this effect after eliciting the context of a clockface-where small numbers are represented on the right and large numbers on the left.

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects.

Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation.

Jewels from chaos: A fascinating journey from abstract forms to physical objects.

An intellectual journey that began with the discovery of strange attractors derived from Chua's circuit, their translation into physical shapes by means of 3D printers, and finally, to the production of jewelry is presented. After giving the mathematical characteristics of Chua's circuit, we explain the chaotic design process, used for creating jewels, providing specifications of the used methodological approach, for its reproduction. We discuss the feasibility of this approach and the transmission of scientific contents on chaos theory, usually restricted to university students, in a high school Science, Technology, Engineering, Art, and Mathematics course, for the realization of advanced educational processes, implemented both in computational and real environments.

Modelling brain dynamics by Boolean networks.

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial-temporal patterns.

A Project Based Learning Approach for Improving Students' Computational Thinking Skills.

An educational robotics lab has been planned for undergraduate students in an Electronic Engineering degree, using the Project Based Learning (PBL) approach and the NAO robot. Students worked in a research context, with the aim of making the functions of the NAO robot as social and autonomous as possible, adopting in the design process the Wolfram Language (WL), from the Mathematica software. Interfacing the programming environment of the NAO with Mathematica, they solved in part the problem of autonomy of the NAO, thus realizing enhanced functions of autonomous movement, recognition of human faces and speech for improving the system social interaction.

SARS-CoV-2 emerging complexity and global dynamics.

The novel SARS-CoV-2 virus, prone to variation when interacting with spatially extended ecosystems and within hosts, can be considered a complex dynamic system. Therefore, it behaves creating several space-time manifestations of its dynamics. However, these physical manifestations in nature have not yet been fully disclosed or understood.

Snarcing with a phone: The role of order in spatial-numerical associations is revealed by context and task demands.

Previous literature on the spatial-numerical association of response codes (SNARC) effect examined which factors modulate spatial-numerical associations. Recently, the role of order in the SNARC effect has been debated, and further research is necessary to better understand its contribution. The present study investigated how the order elicited by the context of the stimuli and by task demands interact.

A unifying nonlinear probabilistic epidemic model in space and time.

Covid-19 epidemic dramatically relaunched the importance of mathematical modelling in supporting governments decisions to slow down the disease propagation. On the other hand, it remains a challenging task for mathematical modelling. The interplay between different models could be a key element in the modelling strategies.

University Students' Hangover May Affect Cognitive Research.

University students are the most employed category of participants in cognitive research. However, researchers cannot fully control what their participants do the night before the experiments (e.g.

On the temporal spreading of the SARS-CoV-2.

The behaviour of SARS-CoV-2 virus is certainly one of the most challenging in contemporary world. Although the mathematical modelling of the virus has made relevant contributions, the unpredictable behaviour of the virus is still not fully understood. To identify some aspects of the virus elusive behaviour, we focused on the temporal characteristics of its course.

Mid-sagittal plane detection for advanced physiological measurements in brain scans.

Objective: The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set.

Approach: To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab based on the k-means clustering function.

Neuroprotective effect of human mesenchymal stem cells in a compartmentalized neuronal membrane system.

Unlabelled: In this work, we describe the development of a compartmentalized membrane system using neonatal rodent hippocampal cells and human mesenchymal stem cells (hMSCs) to investigate the neuroprotective effects of hMSCs. To elucidate this interaction an in vitro oxygen-glucose deprivation (OGD) model was used that mimics central nervous system insults in vivo. Cells were cultured in a membrane system with a sandwich configuration in which the hippocampal cells were seeded on a fluorocarbon (FC) membrane, and were separated by hMSCs through a semipermeable polyethersulfone (PES) membrane that ensures the transport of molecules and paracrine factors, but prevents cell-to-cell contact.

FPGA-based distributed computing microarchitecture for complex physical dynamics investigation.

In this paper, we present a distributed computing system, called DCMARK, aimed at solving partial differential equations at the basis of many investigation fields, such as solid state physics, nuclear physics, and plasma physics. This distributed architecture is based on the cellular neural network paradigm, which allows us to divide the differential equation system solving into many parallel integration operations to be executed by a custom multiprocessor system. We push the number of processors to the limit of one processor for each equation.

Fully automated segmentation of the pons and midbrain using human T1 MR brain images.

Purpose: This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called labs: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction.

Methods: This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects.

A Cellular Neural Network methodology for the automated segmentation of multiple sclerosis lesions.

We present a new application based on genetic algorithms (GAs) that evolves a Cellular Neural Network (CNN) capable of automatically determining the lesion load in multiple sclerosis (MS) patients from magnetic resonance imaging (MRI). In particular, it seeks to identify brain areas affected by lesions, whose presence is revealed by areas of higher intensity if compared to healthy tissue. The performance of the CNN algorithm has been quantitatively evaluated by comparing the CNN output with the expert's manual delineation of MS lesions.

Emergent patterning phenomena in 2D cellular automata.

This article discusses mechanisms of pattern formation in 2D, self-replicating cellular automata (CAs). In particular, we present mechanisms for structure replication that provide insight into analogous processes in the biological world. After examining self-replicating structures and the way they reproduce, we consider their fractal properties and scale invariance.