We consider the exterior as well as the interior free-boundary Bernoulli problem associated with the infinity-Laplacian under a non-autonomous boundary condition. Recall that the Bernoulli problem ...involves two domains: one is given, the other is unknown. Concerning the exterior problem we assume that the given domain has a positive reach, and prove an existence and uniqueness result together with an explicit representation of the solution. Concerning the interior problem, we obtain a similar result under the assumption that the complement of the given domain has a positive reach. In particular, for the interior problem we show that uniqueness holds in contrast to the usual problem associated to the Laplace operator.
The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the ...necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches.
Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the ...availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and other vehicles on the road. The experimental analyses conducted so far have not thoroughly evaluated the effectiveness of these methods in road driving scenarios. To this aim, we propose in this paper a benchmark of modern multi-object detection and tracking methods applied to image sequences acquired by a camera installed on board the vehicle, namely, on the videos available in the BDD100K dataset. The proposed experimental framework allows to evaluate 22 different combinations of multi-object detection and tracking methods using metrics that highlight the positive contribution and limitations of each module of the considered algorithms. The analysis of the experimental results points out that the best method currently available is the combination of ConvNext and QDTrack, but also that the multi-object tracking methods applied on road images must be substantially improved. Thanks to our analysis, we conclude that the evaluation metrics should be extended by considering specific aspects of the autonomous driving scenarios, such as multi-class problem formulation and distance from the targets, and that the effectiveness of the methods must be evaluated by simulating the impact of the errors on driving safety.
Abstract Parkinson's disease is a neurodegenerative disease that causes the death of dopaminergic neurons in the substantia nigra. The resulting dopamine deficiency in the basal ganglia leads to a ...movement disorder that is characterized by classical parkinsonian motor symptoms. Parkinson's disease is recognized as the most common neurodegenerative disorder after Alzheimer's disease. PD ethiopathogenesis remains to be elucidated and has been connected to genetic, environmental and immunologic conditions. The past decade has provided evidence for a significant role of the immune system in PD pathogenesis, either through inflammation or an autoimmune response. Several autoantibodies directed at antigens associated with PD pathogenesis have been identified in PD patients. This immune activation may be the cause of, rather than a response to, the observed neuronal loss. Parkinsonian motor symptoms include bradykinesia, muscular rigidity and resting tremor. The non-motor features include olfactory dysfunction, cognitive impairment, psychiatric symptoms and autonomic dysfunction. Microscopically, the specific degeneration of dopaminergic neurons in the substantia nigra and the presence of Lewy bodies, which are brain deposits containing a substantial amount of α-synuclein, have been recognized. The progression of Parkinson's disease is characterized by a worsening of motor features; however, as the disease progresses, there is an emergence of complications related to long-term symptomatic treatment. The available therapies for Parkinson's disease only treat the symptoms of the disease. A major goal of Parkinson's disease research is the development of disease-modifying drugs that slow or stop the neurodegenerative process. Drugs that enhance the intracerebral dopamine concentrations or stimulate dopamine receptors remain the mainstay treatment for motor symptoms. Immunomodulatory therapeutic strategies aiming to attenuate PD neurodegeneration have become an attractive option and warrant further investigation.
Epigenetic pathways play a pivotal role in the development and function of the immune system. Over the last decade, a growing body of studies has been published out seeking to explain a correlation ...between epigenetic modifications and the development of autoimmune disorders. Epigenetic changes, such as DNA methylation, histone modifications, and noncoding RNAs, are involved in the pathogenesis of autoimmune diseases mainly by regulating gene expression. This paper reviews the importance of epigenetic alterations during the development of the most prevalent human autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSc), Sjogren's syndrome (SS), autoimmune thyroid diseases (AITD), and type 1 diabetes (T1D), aiming to provide new insights in the pathogenesis of autoimmune diseases and the possibility to develop novel therapeutic approaches targeting the epigenome.
Melanoma is one of the most immunologic malignancies based on its higher prevalence in immune-compromised patients, the evidence of brisk lymphocytic infiltrates in both primary tumors and ...metastases, the documented recognition of melanoma antigens by tumor-infiltrating T lymphocytes and, most important, evidence that melanoma responds to immunotherapy. The use of immunotherapy in the treatment of metastatic melanoma is a relatively late discovery for this malignancy. Recent studies have shown a significantly higher success rate with combination of immunotherapy and chemotherapy, radiotherapy, or targeted molecular therapy. Immunotherapy is associated to a panel of dysimmune toxicities called immune-related adverse events that can affect one or more organs and may limit its use. Future directions in the treatment of metastatic melanoma include immunotherapy with anti-PD1 antibodies or targeted therapy with BRAF and MEK inhibitors.
Therapeutic anticoagulation is indicated for a variety of circumstances and conditions in several fields of medicine to prevent or treat venous and arterial thromboembolism. According to the ...different mechanisms of action, the available parenteral and oral anticoagulant drugs share the common principle of hampering or blocking key steps of the coagulation cascade, which unavoidably comes at the price of an increased propensity to bleed. Hemorrhagic complications affect patient prognosis both directly and indirectly (ie, by preventing the adoption of an effective antithrombotic strategy). Inhibition of factor XI (FXI) has emerged as a strategy with the potential to uncouple the pharmacological effect and the adverse events of anticoagulant therapy. This observation is based on the differential contribution of FXI to thrombus amplification, in which it plays a major role, and hemostasis, in which it plays an ancillary role in final clot consolidation. Several agents were developed to inhibit FXI at different stages (ie, suppressing biosynthesis, preventing zymogen activation, or impeding the biological action of the active form), including antisense oligonucleotides, monoclonal antibodies, small synthetic molecules, natural peptides, and aptamers. Phase 2 studies of different classes of FXI inhibitors in orthopedic surgery suggested that dose-dependent reductions in thrombotic complications are not paralleled by dose-dependent increases in bleeding compared with low-molecular-weight heparin. Likewise, the FXI inhibitor asundexian was associated with lower rates of bleeding compared with the activated factor X inhibitor apixaban in patients with atrial fibrillation, although no evidence of a therapeutic effect on stroke prevention is available so far. FXI inhibition could also be appealing for patients with other conditions, including end-stage renal disease, noncardioembolic stroke, or acute myocardial infarction, for which other phase 2 studies have been conducted. The balance between thromboprophylaxis and bleeding achieved by FXI inhibitors needs confirmation in large-scale phase 3 clinical trials powered for clinical end points. Several of such trials are ongoing or planned to define the role of FXI inhibitors in clinical practice and to clarify which FXI inhibitor may be most suited for each clinical indication. This article reviews the rationale, pharmacology, results of medium or small phase 2 studies, and future perspectives of drugs inhibiting FXI.
Late-life cognitive disorders may be prevented by influencing age-related conditions such as frailty, characterized by decreased resistance to stressors and increased risk for adverse health ...outcomes. In the present review article, we examined clinical and epidemiological studies investigating the possible role of different frailty models in modulating the risk of Alzheimer's disease (AD), dementia, vascular dementia (VaD), mild cognitive impairment (MCI), and late-life cognitive impairment/decline that have been published over the past 3 years. Both deficit accumulation and physical frailty models were associated with late-life cognitive impairment/decline, incident dementia, AD, MCI, VaD, non-AD dementias, and AD pathology, proposing cognitive frailty as a new clinical construct with coexisting physical frailty and cognitive impairment in nondemented older subjects. Two subtypes of this new clinical condition have been recently proposed: "potentially reversible" cognitive frailty and "reversible" cognitive frailty. The physical factors should be physical prefrailty and frailty, while the cognitive impairment of potentially reversible cognitive frailty should be MCI (Clinical Dementia rating Scale = 0.5), while the cognitive impairment of reversible cognitive frailty should be pre-MCI Subjective Cognitive Decline (SCD), as recently proposed by the SCD Initiative Working Group. The mechanisms underlying the cognitive-frailty link are multifactorial and vascular, inflammatory, nutritional, and metabolic influences may be of major relevance. Considering both physical frailty and cognition as a single complex phenotype may be crucial in the prevention of dementia and its subtypes with secondary preventive trials on cognitive frail older subjects.
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