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The data science discipline can play a crucial role in developing effective data driven strategies for the valorization and promotion of the cultural heritage (CH) domain. Machine learning approaches can provide new perspectives, allowing knowledge extraction and insights generation from data since in the last decade CH domain has benefited from the applications of internet of things (IoT) solutions in order to improve visitors’ experience. Analyzing a great amount of data increasingly requires the use of advanced mathematical algorithms and therefore requires distribution, calculation and digital protection services. Data represent a great challenge for the CH domain, as well as a resource; this paper presents and discusses the application of a machine learning approach on IoT cultural data collected in the National Archaeological Museum of Naples. With the deployment of some Bluetooth sensing boards we collected the visit paths of the users in a non-invasive way. The research goal is to analyze and classify the collected visiting behavioural data in order to produce useful insights for cultural stakeholders. The knowledge of people behaviours can help museum organizations both in terms of medium-long term strategy and also in terms of strictly operational decisions.

ABSTRACTBackgroundUstekinumab (UST) is an interleukin-12/interleukin-23 receptor antagonist recently approved for treating ulcerative colitis (UC) but with limited real-world data. Therefore, we evaluated the effectiveness and safety of UST in patients with UC in a real-world setting.Research design and methodsThis is a multicenter, retrospective, observational cohort study. The primary endpoints were the clinical remission rate (partial Mayo score, PMS, ≤1) and the safety of UST. Other endpoints were corticosteroid-free remission (CSFR) rate, clinical response rate (PMS reduction of at least 2 points), and fecal calprotectin (FC) reduction at week 24.ResultsWe included 256 consecutive patients with UC (M/F 139/117, median age 52). The clinical remission and clinical response rates at eight weeks were 18.7% (44/235) and 53.2% (125/235), respectively, and 27.6% (42/152) and 61.8% (94/152) at 24 weeks, respectively. At 24 weeks, CSFR was 20.3% (31/152), and FC significantly dropped at week 12 (p = 0.0004) and 24 (p = 0.038). At eight weeks, patients naïve or with one previous biologic treatment showed higher remission (p = 0.002) and clinical >response rates (p = 0.018) than patients previously treated with ≥ 2. Adverse events occurred in six patients (2.3%), whereas four patients (1.6%) underwent colectomy.ConclusionThis real-world study shows that UST effectively and safely treats patients with UC.

Background: Intravenous iloprost has been widely used for the treatment of systemic sclerosis peripheral vasculopathy. No agreement has been found on the regimen and the dosage of intravenous iloprost in different scleroderma subset conditions. This study aimed to evaluate the modalities of intravenous iloprost administration within a large cohort of systemic sclerosis patients from the SPRING Registry and to identify any associated clinical-demographic, instrumental or therapeutic data.Patients and Methods: Data of systemic sclerosis patients treated with intravenous iloprost for at least 1 year (case group) were retrospectively analyzed, including different timing and duration of intravenous iloprost session, and compared with those of untreated patients (control group).Results: Out of 1895 analyzed patients, 937 (49%) received intravenous iloprost treatment, while 958 (51%) were assigned to the control group. Among cases, about 70% were treated every 4 weeks, 24% with an interval of more than 4 weeks, and only 6% of less than 4 weeks. Most patients receiving the treatment every 4 weeks, or less, underwent infusion cycle for 1 day only, while if it was scheduled with an interval of more than 4 weeks, a total number of 5 consecutive days of infusions was the preferred regimen. The comparison between the two groups revealed that patients treated with intravenous iloprost had a higher frequency of DUs (p < 0.001), pitting scars (p < 0.001), diffuse cutaneous involvement (p < 0.001), interstitial lung disease (p < 0.002), as well as higher rates of anti-topoisomerase I, “late” scleroderma patternat nailfold videocapillaroscopy. These findings were confirmed by multivariate analysis.Conclusion: Our data provide a picture on the Italian use of intravenous iloprost among systemic sclerosis patients and showed that it was usually employed in patients with a more aggressive spectrum of the disease. The disparity of intravenous iloprost treatment strategies in the different centers suggests the need of a rational therapeutical approach based on the clinical characteristics of different patients’ subsets.

In recent years, Scientific Machine Learning (SciML) methods for solving Partial Differential Equations (PDEs) have gained increasing popularity. Within such a paradigm, Physics-Informed Neural Networks (PINNs) are novel deep learning frameworks for solving initial-boundary value problems involving nonlinear PDEs. Recently, PINNs have shown promising results in several application fields. Motivated by applications to gas filtration problems, here we present and evaluate a PINN-based approach to predict solutions to strongly degenerate parabolic problems with asymptotic structure of Laplacian type. To the best of our knowledge, this is one of the first papers demonstrating the efficacy of the PINN framework for solving such kind of problems. In particular, we estimate an appropriate approximation error for some test problems whose analytical solutions are fortunately known. The numerical experiments discussed include two and three-dimensional spatial domains, emphasizing the effectiveness of this approach in predicting accurate solutions.

In this paper we aim to compare a popular numerical method with a new, recently proposed meshless approach for Heston PDE resolution. In finance, most famous models can be reformulated as PDEs, which are solved by finite difference and Monte Carlo methods. In particular, we focus on Heston model PDE and we solve it via radial basis functions(RBF) methods and alternating direction implicit. RBFs have become quite popular in engineering as meshless methods: they are less computationally heavy than finite differences and can be applied for high-order problems.

DNA double-strand breaks (DSBs) contribute to genome instability, a key feature of cancer. DSBs are mainly repaired by homologous recombination (HR) and non-homologous end-joining (NHEJ). We investigated the role of an isoform of the multifunctional cyclin-dependent kinase 9, CDK9-55, in DNA repair, by generating CDK9-55-knockout HeLa clones (through CRISPR-Cas9), which showed potential HR dysfunction. A phosphoproteomic screening in these clones treated with camptothecin revealed that CDC23 (cell division cycle 23), a component of the E3-ubiquitin ligase APC/C (anaphase-promoting complex/cyclosome), is a new substrate of CDK9-55, with S588 being its putative phosphorylation site. Mutated non-phosphorylatable CDC23(S588A) affected the repair pathway choice by impairing HR and favouring error-prone NHEJ. This CDK9 role should be considered when designing CDK-inhibitor-based cancer therapies.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user-level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps, automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative $Q$Q-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

ABSTRACTIncreasingly, social scientists are recognizing the limitations of traditional measures (e.g., geographic, demographic, economic) when trying to explain differing regional prosperity outcomes. This research seeks to understand how regions’ differing personalities can help describe economic variance. We test this by employing least squares linear regression on an exploratory battery of 16 psychosocial variables (the “Big 5” personality profiles, plus other General Social Survey items) and four dependent variables of economic output: per capita income, employment rates, income mobility, and rates of entrepreneurship. All items, aggregated at the county level across the US, exhibited a unique constellation of relationships, emphasizing the great need for more work on the economic impact of what we coin the personality of place.

Purpose: This paper aims to investigate the cultural meanings of excessive drinking in three different countries with different levels of alcohol use chosen as case studies of wider geographies representing Northern (Denmark), Southern (Italy) and Eastern (Estonia) Europe. Design/methodology/approach: Data were collected according to the Reception Analytical Group Interview method, using video clips as stimuli to enhance comparability. Eight online focus groups were organized in each country for a total number of 128 participants. Symbolic boundaries defining what drinking patterns are socially acceptable were then analysed to look at cross-national variations. Findings: Results show how different conceptualizations of excessive drinking persist, although a convergence process among drinking patterns is also observed, which suggests that differences mainly depend on meanings and values attributed to intoxication. These are both rooted in the traditional drinking cultures and affected by ongoing social and economic change processes. Research limitations/implications: Because of the chosen research approach, the research results may lack generalizability, even at country level, as there are differences also within the same drinking culture; however, addressing these differences was beyond the scope of the present study, which aimed to contribute to understanding persisting differences in European drinking culture despite different drivers seem to act for globalization of drinking habits. Practical implications: The paper includes implications for the development of tailored and effective prevention messages, considering rooted attitudes and cultural values attached to drinking and drunkenness in different European geographies, which are also related to conceptualizations of risks and pleasure. Originality/value: This paper fulfils an identified need to understand persisting differences in alcohol-related behaviours and outcome in different European countries emerging from quantitative data.

ABSTRACTIntroductionThe coronavirus (COVID-19) pandemic has led to as well as exacerbated mental health disorders, leading to increased use of psychotropic medications. Co-administration of COVID-19 and psychotropic medications may result in drug-drug interactions (DDIs), that may compromise both the safety and efficacy of both medications.Areas coveredThis review provides an update of the current evidence on DDIs between COVID-19 and psychotropic medications. The interactions are categorized into pharmacokinetic, pharmacodynamic, and other relevant types. A thorough literature search was conducted using electronic databases to identify relevant studies, and extract data to highlight potential DDIs, clinical implications, and management strategies.Expert opinionUnderstanding and managing potential DDIs between COVID-19 and psychotropic medications is paramount to ensuring safe and effective treatment of patients with COVID-19 and mental illness. Awareness of the diverse spectrum of DDIs, vigilant monitoring, and judicious dose modifications, while choosing pharmacotherapeutic options with low risk of interaction whenever possible, are necessary. Ongoing and future investigations should continue to review the dynamic landscape of COVID-19 therapeutic modalities and their interactions with psychotropic medications.

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.