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Dementia refers to a group of neurodegenerative disorders that impact the cognitive function of an increasing number of individuals. Because of the variety of manifestations, the idea of mixed dementia has recently garnered increased awareness and attention from the scientific community. In this work, we describe a high-quality dataset, as well as the findings of a preliminary analysis devoted to investigating the potential of computational methods that are highly indicative of mixed dementia. We will specifically describe the findings of a phenotypic stratification analysis, based on clustering approaches, that highlights possibly significant aspects of mixed dementia, paving the way for further research devoted to the application of Machine Learning techniques to the robust and early diagnosis of mixed dementia.

Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty, this problem still remains an unsolved open issue especially in a multidimensional perspective. In this work we present our proposal to face multidimensional poverty in case of a fragile population, the older adults, starting from an unlabelled dataset, collected administering a proper questionnaire to about 500 individuals. Firstly a model that allows to label the collected data into three classes of poverty is proposed. Then, XGBoost and Naive Bayes classifiers are considered to solve the classification problem. Finally, after having determined the relative importance of each feature, a novel Naive Bayes model is proposed that relies on new aggregated features that represent five poverty dimensions. These aggregated features are obtained by properly combining the variables collected through the questionnaire with cut-offs defined by a domain expert.

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography (EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients’ engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to both the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute the data without requiring long or repeated EEG recordings. Moreover, substituting faulty trials may significantly improve the classification performances of a BCI when translating imagined movement to rehabilitation systems. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection criterion based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.

Poverty is a multidimensional concept that, besides the economic status and financial resources, should consider the lack of access to resources enabling a minimum standard of living and participation in society. In particular, elderly people are likely to require help with some or everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health. This work proposes a strategy based on Bayesian Network to identify the risk of poverty in elderly people, relying on multidimensional indicators learned from heterogeneous sources of information, including the difficulty of accessing services, social exclusion and health status. Data cleaning and integration that include socio-demographic indicators are proposed here, and an overall framework of analysis that can be exportable to several other categories of the population at risk of poverty is presented.

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography (EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients’ engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to both the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute the data without requiring long or repeated EEG recordings. Moreover, substituting faulty trials may significantly improve the classification performances of a BCI when translating imagined movement to rehabilitation systems. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection criterion based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.

Many manifestations of interactive human behavior (social and with the environment) are conditioned by emotions, influencing reasoning and other rational decision making activities. The study of the interplay of emotional and non-emotional behaviors (spatial motion) is here faced through the modeling of affective agents where affective states are explicitly measured and represented thanks to the collection of data in a dedicated experiment with humans. During this experiment, we observed that subjects of different ages (focusing on elderly) react differently to particular spatial stimuli (proxemics distance calculation), manifesting a strict relation between distancing and emotional states. The agent-based modeling and simulation of this behavior here presented is a contribution to the comprehension of complex interplays between spatial distances and affective states, amplified by the recent experience of pandemic, where aware distancing become a mandatory and affecting factor of the life, especially for fragile and aged people. The presented modeling approach relies on data collected with an online experiment performed to understand what kind of personal, psychological and situational factors influenced people’s behavior while distancing from others, in particular during the COVID-19 pandemic. The focus of the experiment was in the comparison of different age reactions, involving 80 participants aged between 16 and 92 years.

Introduction: Parkinson's disease (PD) is one of the most frequent causes of disability among older people, characterized by motor disorders, rigidity, and balance problems. Recently, dance has started to be considered an effective exercise for people with PD. In particular, Irish dancing, along with tango and different forms of modern dance, may be a valid strategy to motivate people with PD to perform physical activity. The present protocol aims to implement and evaluate a rehabilitation program based on a new system called “SI-ROBOTICS,” composed of multiple technological components, such as a social robotic platform embedded with an artificial vision setting, a dance-based game, environmental and wearable sensors, and an advanced AI reasoner module. Methods and Analysis: For this study, 20 patients with PD will be recruited. Sixteen therapy sessions of 50 min will be conducted (two training sessions per week, for 8 weeks), involving two patients at a time. Evaluation will be primarily focused on the acceptability of the SI-ROBOTICS system. Moreover, the analysis of the impact on the patients' functional status, gait, balance, fear of falling, cardio-respiratory performance, motor symptoms related to PD, and quality of life, will be considered as secondary outcomes. The trial will start in November 2021 and is expected to end by April 2022. Discussions: The study aims to propose and evaluate a new approach in PD rehabilitation, focused on the use of Irish dancing, together with a new technological system focused on helping the patient perform the dance steps and on collecting kinematic and performance parameters used both by the physiotherapist (for the evaluation and planning of the subsequent sessions) and by the system (to outline the levels of difficulty of the exercise). Ethics and Dissemination: The study was approved by the Ethics Committee of the IRCCS INRCA. It was recorded in ClinicalTrials.gov on the number NCT05005208. The study findings will be used

While mitochondrial dysfunction is acknowledged as a major feature of aging, much less is known about the role of mitochondria in extended longevity. Livers from aged (28-month-old) and extremely aged (32-month-old) rats were analyzed for citrate synthase activity, mitochondrial transcription factor A (TFAM) amount, mitochondrial DNA (mtDNA), and 4.8 Kb “common deletion” contents. None of the assayed parameters differed significantly between age groups. TFAM-binding to mtDNA and the incidence of 8-oxo-deoxyguanosine in specific mtDNA regions, encompassing the origins of mtDNA replication (D-loop and Ori-L) and the 16-bp long direct repeat 1 (DR1) of the 4.8 Kb deletion, were determined. A decrease in TFAM binding was unveiled at all regions in extremely aged in comparison with aged rats. Reduced incidence of oxidized purines at all assayed regions was detected in 32-month-old rats compared with the 28-month-old group. A significant positive correlation between the incidence of 8-oxo-deoxoguanosine and TFAM-bound mtDNA was found at D-Loop and Ori-L regions only in 28-month-old rats. The absence of such correlation in 32-month-old rats indicates a different, fine-tuned regulation of TFAM binding in the two age groups and supports the existence of two different paces in aging and extended aging.

The well-known age-related mitochondrial dysfunction deeply affects heart because of the tissue's large dependence on mitochondrial ATP provision. Our study revealed in aged rat heart a significant 25% decrease in mtDNA relative content, a significant 29% increase in the 4.8 Kb mtDNA deletion relative content, and a significant inverse correlation between such contents as well as a significant 38% decrease in TFAM protein amount. The TFAM-binding activity to specific mtDNA regions increased at those encompassing the mtDNA replication origins, D-loop and Ori-L. The same mtDNA regions were screened for different kinds of oxidative damage, namely Single Strand Breaks (SSBs), Double Strand Breaks (DSBs), abasic sites (AP sites) and oxidized bases as 7,8-dihydro-8-oxoguanine (8oxoG). A marked increase in the relative content of mtDNA strand damage (SSBs, DSBs and AP sites) was found in the D-loop and Ori-L regions in the aged animals, unveiling for the first time in vivo an age-related, non-stochastic accumulation of oxidative lesions in these two regions that appear as hot spots of mtDNA damage. The use of Formamidopyrimidine glycosylase (Fpg) demonstrated also a significant age-related accumulation of oxidized purines particularly in the D-loop and Ori-L regions. The detected increased binding of TFAM to the mtDNA damage hot spots in aged heart suggests a link between TFAM binding to mtDNA and loss of mitochondrial genome likely through hindrance of repair processes.