The role of the opioid system in binge eating disorder.

Binge eating disorder is characterized by excessive, uncontrollable consumption of palatable food within brief periods of time. Excessive intake of palatable food is thought to be driven by hedonic, rather than energy homeostatic, mechanisms. However, reward processing does not only comprise consummatory actions; a key component is represented by the anticipatory phase directed at procuring the reward. This phase is highly influenced by environmental food-associated stimuli, which can robustly enhance the desire to eat even in the absence of physiological needs. The opioid system (endogenous peptides and their receptors) has been strongly linked to the rewarding aspects of palatable food intake, and perhaps represents the key system involved in hedonic overeating. Here we review evidence suggesting that the opioid system can also be regarded as one of the systems that regulates the anticipatory incentive processes preceding binge eating hedonic episodes.CG was funded by Medical Research Council Programme Grant (no. G1002231) and PC was funded by the National Institute on Drug Abuse (NIDA/NIH, no. DA030425) and the National Institute of Mental Health (NIMH/NIH, no. MH091945).This is the author accepted manuscript. The final version is available from Cambridge University Press via http://dx.doi.org/10.1017/S109285291500066

Structural Knowledge Extraction and Representation in Sensory Data

During the last decades the availability of increasingly cheaper technology for pervasive monitoring has boosted the creation of systems able to automatically comprehend the events occurring in the monitored area, in order to plan a set of actions to bring the environment closer to the user's preferences. These systems must inevitably process a great amount of raw data - sensor measurements - and need to summarize them in a high-level representation to accomplish their tasks. An implicit requirement is the need to learn from experience, in order to be able to capture the hidden structure of the data, in terms of relations between its key components. The availability of large collections of data, however, has increased the awareness that "measuring" does not seamlessly translate into "understanding", and more data does not entail more knowledge. Scientific literature documents a massive use of Statistical Machine Learning in almost all data analysis and data mining applications, aiming at minimizing the need for a-priori knowledge. A remarkable drawback of such algorithms, however, is their failure to effortlessly provide insight about the most significant features of the data, as they typically just provide optimal parameter settings for a "black-box". In this thesis, it is claimed that structure is the key to handle the complexity of acquiring knowledge from unstructured data in real-life scenarios. A shift in perspective will allow to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples; more specifically, the suggestion is to state this process within the framework of formal languages and automata borrowing concepts and methods from Algorithmic Learning Theory. In this context, knowledge extraction may be turned into structural pattern identification, letting syntactic models emerge from data itself. In order to prove the soundness of this proposal, three different case studies will be presented, exploiting statistical learning, syntactical methods and formal languages, respectively. The third approach will be particularly useful to highlight the advantage of building intrinsically recursive models, which give multi-scale - more natural - representations; as a result, the computational burden that characterizes the huge volume of data will be lessened. Moreover, the task of designing reliable and efficient automatic systems for knowledge extraction can be alleviated by using such human-understandable models.During the last decades the availability of increasingly cheaper technology for pervasive monitoring has boosted the creation of systems able to automatically comprehend the events occurring in the monitored area, in order to plan a set of actions to bring the environment closer to the user's preferences. These systems must inevitably process a great amount of raw data - sensor measurements - and need to summarize them in a high-level representation to accomplish their tasks. An implicit requirement is the need to learn from experience, in order to be able to capture the hidden structure of the data, in terms of relations between its key components. The availability of large collections of data, however, has increased the awareness that "measuring" does not seamlessly translate into "understanding", and more data does not entail more knowledge. Scientific literature documents a massive use of Statistical Machine Learning in almost all data analysis and data mining applications, aiming at minimizing the need for a-priori knowledge. A remarkable drawback of such algorithms, however, is their failure to effortlessly provide insight about the most significant features of the data, as they typically just provide optimal parameter settings for a "black-box". In this thesis, it is claimed that structure is the key to handle the complexity of acquiring knowledge from unstructured data in real-life scenarios. A shift in perspective will allow to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples; more specifically, the suggestion is to state this process within the framework of formal languages and automata borrowing concepts and methods from Algorithmic Learning Theory. In this context, knowledge extraction may be turned into structural pattern identification, letting syntactic models emerge from data itself. In order to prove the soundness of this proposal, three different case studies will be presented, exploiting statistical learning, syntactical methods and formal languages, respectively. The third approach will be particularly useful to highlight the advantage of building intrinsically recursive models, which give multi-scale - more natural - representations; as a result, the computational burden that characterizes the huge volume of data will be lessened. Moreover, the task of designing reliable and efficient automatic systems for knowledge extraction can be alleviated by using such human-understandable models

Withdrawal from Extended, Intermittent Access to A Highly Palatable Diet Impairs Hippocampal Memory Function and Neurogenesis: Effects of Memantine.

BACKGROUND: Compulsive eating can be promoted by intermittent access to palatable food and is often accompanied by cognitive deficits and reduction in hippocampal plasticity. Here, we investigated the effects of intermittent access to palatable food on hippocampal function and neurogenesis. METHODS: Male Wistar rats were either fed chow for 7 days/week (Chow/Chow group), or fed chow intermittently for 5 days/week followed by a palatable diet for 2 days/week (Chow/Palatable group). Hippocampal function and neurogenesis were assessed either during withdrawal or following renewed access to palatable food. Furthermore, the ability of the uncompetitive N-methyl-d-aspartate receptor (NMDAR) antagonist memantine to prevent the diet-induced memory deficits and block the maladaptive feeding was tested. RESULTS: Palatable food withdrawn Chow/Palatable rats showed both a weakened ability for contextual spatial processing and a bias in their preference for a "novel cue" over a "novel place," compared to controls. They also showed reduced expression of immature neurons in the dentate gyrus of the hippocampus as well as a withdrawal-dependent decrease of proliferating cells. Memantine treatment was able both to reverse the memory deficits and to reduce the excessive intake of palatable diet and the withdrawal-induced hypophagia in food cycling rats. CONCLUSIONS: In summary, our results provide evidence that withdrawal from highly palatable food produces NMDAR-dependent deficits in hippocampal function and a reduction in hippocampal neurogenesis

Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique

Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

Objective: This study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS). Methods: We retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results. Results: Of 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (<1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20-12.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses. Interpretation: This study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context

Background and objectives: It is unclear how multiple sclerosis (MS) affects the severity of COVID-19. The aim of this study is to compare COVID-19-related outcomes collected in an Italian cohort of patients with MS with the outcomes expected in the age- and sex-matched Italian population. Methods: Hospitalization, intensive care unit (ICU) admission, and death after COVID-19 diagnosis of 1,362 patients with MS were compared with the age- and sex-matched Italian population in a retrospective observational case-cohort study with population-based control. The observed vs the expected events were compared in the whole MS cohort and in different subgroups (higher risk: Expanded Disability Status Scale [EDSS] score > 3 or at least 1 comorbidity, lower risk: EDSS score ≤ 3 and no comorbidities) by the χ2 test, and the risk excess was quantified by risk ratios (RRs). Results: The risk of severe events was about twice the risk in the age- and sex-matched Italian population: RR = 2.12 for hospitalization (p < 0.001), RR = 2.19 for ICU admission (p < 0.001), and RR = 2.43 for death (p < 0.001). The excess of risk was confined to the higher-risk group (n = 553). In lower-risk patients (n = 809), the rate of events was close to that of the Italian age- and sex-matched population (RR = 1.12 for hospitalization, RR = 1.52 for ICU admission, and RR = 1.19 for death). In the lower-risk group, an increased hospitalization risk was detected in patients on anti-CD20 (RR = 3.03, p = 0.005), whereas a decrease was detected in patients on interferon (0 observed vs 4 expected events, p = 0.04). Discussion: Overall, the MS cohort had a risk of severe events that is twice the risk than the age- and sex-matched Italian population. This excess of risk is mainly explained by the EDSS score and comorbidities, whereas a residual increase of hospitalization risk was observed in patients on anti-CD20 therapies and a decrease in people on interferon

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

Gaining insight by structural knowledge extraction

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside the system itself; however, the designer is expected to intuitively grasp the most relevant features of the raw data as a. preliminary step. In this paper we propose a framework to get useful insight about a set of complex data, and we claim that a shift in perspective may be of help to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples. We will present a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples, and a proof-of-concept application in a scenario of mobility data

Gl-learning: an optimized framework for grammatical inference

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Bluethat significantly broadens the range of the potential application scenarios for grammar models. The modular design of our C++ library makes it an efficient and extensible framework for the design of further novel algorithms