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. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

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. We think that the idea of transforming science into art forms can drive students in acquiring scientific knowledge and skills, allowing them to discover the inner beauty of chaos.

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. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

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. An external repository was created to streamline processes and stow data that the robot can easily access. Self-assessment processes demonstrated that the course provided students with useful skills to cope with real life problems. Cognitive aspects of programming by WL have also been collected in the students' feedback.

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. Here we propose a continuous space-time non-linear probabilistic model from which we can derive many of the existing models both deterministic and stochastic as for example SI, SIR, SIR stochastic, continuous-time stochastic models, discrete stochastic models, Fisher-Kolmogorov model. A partial analogy with the statistical interpretation of quantum mechanics provides an interpretation of the model. Epidemic forecasting is one of its possible applications; in principle, the model can be used in order to locate those regions of space where the infection probability is going to increase. The connection between non-linear probabilistic and non-linear deterministic models is analyzed. In particular, it is shown that the Fisher-Kolmogorov equation is connected to linear probabilistic models. On the other hand, a generalized version of the Fisher-Kolmogorov equation is derived from the non-linear probabilistic model and is shown to be characterized by a non-homogeneous time-dependent diffusion coefficient (anomalous diffusion) which encodes information about the non-linearity of the probabilistic model.

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. Here we show 4D and 2D space-time patterns of the rate of infected individuals on a global scale, giving quantitative measures of transitions between different dynamical behaviors. By slicing the spatiotemporal patterns, we found manifestations of the virus behavior, such as cluster formation and bifurcation. Furthermore, by analyzing morphogenesis processes by entropy, we have been able to detect the virus phase transitions, typical of adaptive biological systems. Our results for the first time describe the virus patterning behavior processes all over the world, giving them quantitative measures. We know that the outcomes of this work are still partial and more advanced analyses of the virus behavior in nature are necessary. However, we think that the set of methods implemented can provide significant advantages to better analyze the viral behavior in the approach of system biology, thus expanding knowledge and improving pandemic problem solving.

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. We have analysed the latency trends the virus has realized worldwide, the outbreak of the hot spots, and the decreasing trends of the pandemic. We found that the spatio-temporal pandemic dynamics shows a complex behaviour. As with physical systems, these changes in the pandemic's course, which we have called transitional stages of contagion, highlight shared characteristics in many countries. The main results of this work is that the pandemic progression rhythms have been clearly identified for each country, providing the processes and the stages at which the virus develops, thus giving important information on the activation of containment and control measures.

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. The results have been compared with the evaluation of four experts who manually identified the mid-sagittal plane and whose performances have been combined with a cognitive decisional algorithm in order to define a gold standard. MAIN RESULTS: The comparison provided a mean percentage error of 1.84%. To further refine the automatic procedure we trained a machine learning system using the results from the proposed algorithm and the gold standard. We tested this machine learning system and obtained results comparable to medical raters with a mean absolute error of 1.86 slices. SIGNIFICANCE: The system is promising and could be directly incorporated into broader diagnostic support systems.

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

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. This system was used to simulate a cerebral ischemic damage by inducing OGD for 120min. The core contribution of the work highlights the neuroprotective effects of hMSCs on hippocampal cells in a membrane system for the first time. The novel results show that hMSC secretome factors protect hippocampal cells against OGD insults as indicated by the conservation of specific structural and functional cell features together with the development of a highly branched neural network after the damage. Moreover, neuronal cells co-cultured with hMSCs before OGD insult were able to maintain BDNF production and O2 consumption and did not express the apoptotic markers that were expressed in similarly insulted neuronal cells that had not been co-cultured with hMSCs. This compartmentalized membrane system appears to be a very useful and reliable system for studying the neuroprotective effects of hMSCs and identifying secreted factors that may be involved. STATEMENT OF SIGNIFICANCE: This paper is based on a combined synergism of biomaterials technology and stem cell approach, focusing on the development of a compartmentalized membrane system that serves as an innovative tool for highlighting the role of hMSCs on hippocampal neurons upon damage. The membrane system consists of two different flat sheet membranes, giving rise to double and separated cell membrane compartments that prevent cell-to-cell contact but allow the transport of paracrine factors. This system strongly corroborates the paracrine mediated neuroprotection of hMSCs on ischemic damaged neurons. The challenging and pioneeristic approach by using biomaterials allowed to perform a stepwise analysis of the phenomena, providing new insights into the field of MSC therapy.

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. Its reliability was further confirmed by including neurological patients (with Alzheimer's Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. RESULTS: The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024-1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97-0.98 and Hausdorff distance ranging 1.07-1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86-0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71-2.15). CONCLUSIONS: Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.

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. In order to test the present idea, we choose to implement DCMARK on a single FPGA, designing the single processor in order to minimize its hardware requirements and to obtain a large number of easily interconnected processors. This approach is particularly suited to study the properties of 1-, 2- and 3-D locally interconnected dynamical systems. In order to test the computing platform, we implement a 200 cells, Korteweg-de Vries (KdV) equation solver and perform a comparison between simulations conducted on a high performance PC and on our system. Since our distributed architecture takes a constant computing time to solve the equation system, independently of the number of dynamical elements (cells) of the CNN array, it allows us to reduce the elaboration time more than other similar systems in the literature. To ensure a high level of reconfigurability, we design a compact system on programmable chip managed by a softcore processor, which controls the fast data/control communication between our system and a PC Host. An intuitively graphical user interface allows us to change the calculation parameters and plot the results.

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. The CNN algorithm was run on a data set of 11 MS patients; for each one a single dataset of MRI images (matrix resolution of 256×256 pixels) was acquired. Our automated approach gives satisfactory results showing that after the learning process the CNN is capable of detecting MS lesions with different shapes and intensities (mean DICE coefficient=0.64). The system could provide a useful support tool for the evaluation of lesions in MS patients, although it needs to be evolved and developed in the future.

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. We explore the space of all possible mutations, showing that despite their apparent differences, many patterns produced by CAs are based on universal models of development and that mutations may lead either to stable or to unstable development dynamics. An example of this process for all possible one-step mutations of one specific CA is given. We have demonstrated that a self-replicating system can carry out many slightly different but related entities, realizing new different growth models. We infer that self-replicating systems exist in an intermediate regime between order and chaos, showing that these models degrade into chaotic configurations, passing through a series of transition stages. This process is quantified by measuring the Hamming distances between the pattern produced by the original self-replicator and those produced by mutated systems. The analysis shows that many different mechanisms may be involved in patterning phenomena. These include changes in the external or internal layers of the structure, substitution of elements, differential rates of growth in different parts of the structure, structural modifications, changes in the original model, the emergence of different structures governed by different CA rules, and changes in the self-replication process.