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Migraine is a common neurological disorder impairing the quality of life of patients. The condition requires, as an acute or prophylactic line of intervention, the frequent use of drugs acting on the central nervous system (CNS). The long-term impact of these medications on cognition and neurodegeneration has never been consistently assessed. The paper reviews pharmacological migraine treatments and discusses their biological and clinical effects on the CNS. The different anti-migraine drugs show distinct profiles concerning neurodegeneration and the risk of cognitive deficits. These features should be carefully evaluated when prescribing a pharmacological treatment as many migraineurs are of scholar or working age and their performances may be affected by drug misuse. Thus, a reconsideration of therapy guidelines is warranted. Furthermore, since conflicting results have emerged in the relationship between migraine and dementia, future studies must consider present and past pharmacological regimens as potential confounding factors.

A 43-year-old came to our observation for progressive cognitive impairment, confirmed by the neuropsychological evaluation. A diagnosis of multidomain amnestic mild cognitive impairment, due to unknown reasons, was posited at the first assessment. The patient’s neurological exam was otherwise completely normal. The patient’s mother was clinically diagnosed with frontotemporal dementia in her forties. The patient underwent neuroimaging investigations and cerebrospinal fluid analysis. Our diagnostic work-up pointed toward a neurodegenerative etiology, but the presence of concurrent cardiomyopathy emerged in the meantime. Due to the patient’s family history, a thorough genetic screening was performed. The results revealed a unique genetic asset, with heterozygotic variants of three amyloid-related genes (PSEN1, APP, and MYBPC3). PSEN1 and MYBPC3 mutations showed distinct pathogenic features and accounted for the patient’s brain and cardiac amyloidosis, whereas the APP variant was of uncertain pathological implications.

Sars-CoV-2 is a respiratory virus that can access the central nervous system, as indicated by the presence of the virus in patients' cerebrospinal fluid and the occurrence of several neurological syndromes during and after COVID-19. Growing evidence indicates that Sars-CoV-2 can also trigger the acute onset of mood disorders or psychotic symptoms. COVID-19-related first episodes of mania, in subjects with no known history of bipolar disorder, have never been systematically analyzed. Thus, the present study assesses a potential link between the two conditions. This systematic review analyzes cases of first appearance of manic episodes associated with COVID-19. Clinical features, pharmacological therapies, and relationships with pre-existing medical conditions are also appraised. Medical records of twenty-three patients fulfilling the current DSM-5 criteria for manic episode were included. Manic episodes started, on average, after 12.71±6.65 days from the infection onset. Psychotic symptoms were frequently reported. 82.61% of patients exhibited delusions, whereas 39.13% of patients presented hallucinations. A large discrepancy in the diagnostic workups was observed. Mania represents an underestimated clinical presentation of COVID-19. Further studies should focus on the pathophysiological substrates of COVID-19-related mania and pursue appropriate and specific diagnostic and therapeutic workups.

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of (r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44robots are provided to corroborate the theoretical findings.

In this paper, we address the problem of stable coordinated motion in multi-robot systems with limited fields of view (FOVs). These problems arise naturally for multi-robot systems that interact based on sensing, such as our case study of multiple unmanned aerial vehicles (UAVs) each equipped with several cameras that are used for detecting neighboring UAVs.

In this paper we study a distributed optimization problem for continuous time multi-agent systems. In our setting, the global objective for the multi-agent system is to minimize the sum of locally coupled strictly convex cost functions. Notably, this class of optimization objectives can be used to encode several important problems such as distributed estimation. For this problem setting, we propose a distributed signed gradient descent algorithm, which relies on local observers to retrieve 2-hop state information that are required to compute the descent direction. Adaptive gains for the local observer are introduced to render the convergence independent from: i) the structure of the network topology and ii) the local gains of the per-agent signed gradient-descent update law. The finite-time convergence of the local observer and of the proposed signed gradient descent method is demonstrated. Numerical simulations involving a distributed weighted least-square (WLS) estimation problem, with the aim of identifying in the context of an advanced water management system for precision-farming the soil thermal properties in a large-scale hazelnut orchard, have been proposed to corroborate the theoretical findings.

Optimization over Markov chains is a popular topic, since Markov chains can be used to model several important systems, such as telecommunications, railways or dams. However, most of the existing literature focuses on steady states and limiting distributions, while in some contexts it might be beneficial to design Markov chains with repulsive distributions, i.e., distributions that, once achieved, cause a large probability transition in the system. This, as typically done in Critical Infrastructure systems, corresponds to having resilient Markov chains that 'spring back' after reaching potentially dangerous or unsafe configurations. In this paper we formulate a problem where we aim at redesigning a Markov chain in order from one side to maximize the repulsiveness of a prescribed distribution and from another side to minimize the redesign effort. In more detail, we formulate the problem as an optimization problem, and we provide a necessary and sufficient local optimality condition and a sufficient global optimality condition. We conclude the paper with simulations aimed at numerically demonstrating the theoretical findings in the paper.