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Vascular graft infection (VGI) is a rare but severe complication of vascular surgery that is associated with a bad prognosis and high mortality rate. An accurate and prompt identification of the infection and its extent is crucial for the correct management of the patient. However, standardized diagnostic algorithms and a univocal consensus on the best strategy to reach a diagnosis still do not exist. This review aims to summarize different radiological and Nuclear Medicine (NM) modalities commonly adopted for the imaging of VGI. Moreover, we attempt to provide evidence-based answers to several practical questions raised by clinicians and surgeons when they approach imaging in order to plan the most appropriate radiological or NM examination for their patients.

This Special Issue is centered on a fundamental research question that characterizes the comparative studies on these topics: where are the legal professions heading? The focus is therefore on past, present, and, especially, future of the legal professions, both in Europe and outside Europe, in civil law and common law systems, in the north and south of the world. The legal professions, in fact, are facing important challenges, as expecially the issue of their internal heterogeneity — e.g., the gap between the now dominant business lawyers and the judicial bar, each one with different interests, ethics and regulations. This Special Issue aims to contribute to the current interdisciplinary debate on the legal professions, which represent the pivotal “protagonists” of interpretation and application of law, delivery of justice and protection of people’s rights.

In several Western European countries, a significant share of social rental housing stock has been sold since the 1980s as part of government policies aimed at promoting homeownership societies. Research has shown that tenure conversion has contributed to increasing socio-spatial segregation of lower-income groups, with diverging spatial patterns of homeownership among immigrants. This paper examines the impact of recent public housing privatization schemes in Milan in relation to micro-segregation and peripheralization processes of foreign populations, which represent distinctive features of immigrant residential distribution in this city. By employing name analysis, an unconventional approach in segregation studies, I inferred the geographical origins of homebuyers and mapped their distribution across the city. The findings reveal divergent purchasing behaviors, whereby Italians predominantly acquire properties in semi-central areas currently undergoing urban regeneration. In contrast, immigrants tend to concentrate their acquisitions in peripheral post-war public housing neighborhoods or in areas predominantly inhabited by residents with similar geographical origins. This paper contributes to the existing literature on ethnic residential segregation in Southern European cities by shedding light on the underexplored role of public housing privatization policies in shaping specific residential patterns and housing outcomes among different groups.

Untreated oral diseases are detrimental to overall well-being and quality of life and are in close relationship with social and economic consequences. The presence of strong evidence for caries primary and secondary prevention is a compulsory tool for the development of clinical practice guidelines (CPGs). This paper was aimed to assess systematically the importance of clinical practice guidelines in caries prevention management considering both the adult and pediatric populations and evaluate them using the Appraisal of Guidelines for Research and Evaluation (AGREE II) Checklist. Records were extracted from EMBASE, SCOPUS, PubMed/Medline and seven other relevant guideline databases between 6 January and 14 February 2023. Two reviewers independently conducted the appraisal using the web-based platform My AGREE PLUS. Twenty-one guidelines/papers met the inclusion criteria and were reviewed. Eight CPGs included both primary and secondary prevention interventions, whereas thirteen presented a single preventive model. Overall, 12 guidelines were published in the USA. The mean AGREE II scores ranged from 35.4% to 84.3%. Of the total twenty-one included guidelines, twelve were classified as "Recommended", ranging from 56.3% to 84.3%, the others were described as "Recommended with modification", ranging from 35.4% to 68.9%. From the AGREE II analysis carried out, the CPGs included in this survey adopted a punctual methodological rigor but lacked applicative power. The present survey showed that the public, as the primary beneficiary, played a limited role in the development of the twenty-one CPGs. Hence, methodological improvement can better support high-quality CPG development in the future.

This paper considers the personal commitment to ‘boxe popolare’ (people’s boxing), focusing on my scholar-practitioner status as a tool to contribute to the boxe popolare agenda by means of what I term ‘shared sociological imagination’. Through a reflexive tale on becoming a boxe popolare member, the article sheds light on the importance of overcoming the theory/practice divide. The first section of the paper draws on ‘habitus as topic and tool’—namely, the methodology I have adopted in a four-year ethnography of boxe popolare—and illustrates sociological imagination as a capacity that can be cultivated even in extremely carnal worlds by social agents who do not belong to academia. The second section broadens the reasoning, arguing that one characterising trait of being a scholar-practitioner in sport and physical culture may consist in working out agency both on an individual and a collective level. Echoing Burawoy’s perspective of ‘public sociology’, such an attempt can be seen as a potentially emancipatory strategy: it allows people with whom we research and practice to live with and through theory, embodying shared understandings in novel mundane activities.

Resilience and the efficiency of transportation systems are crucial for the economic development of geographical areas, and network analysis applied to railways can provide insight into the importance of branch lines and their impacts on the entire system. This paper explores the behavior of the ERC measure, a local robustness measure, on the railway network in Lombardy, Italy, and analyzes the impacts of deactivating stations or journeys on the network’s robustness. Changes in the topological properties of the network were studied by simulating potential external disturbances and analyzing the impact of deleting the most connected stations or railway lines. The numerical results show how the measures provided effectively identify critical stations and journeys within the network structure and outperform classical topological metrics. Since ERC measures take into account all of the alternative paths present in the network, they can provide valuable information for rerouting traffic along alternative paths in case of failures or disruptions. The paper’s original contribution lies in demonstrating the effectiveness of the ERC measure in identifying critical stations and journeys within the network structure.

BCI devices are increasingly being used to create interactive interfaces between users and their own psychophysiological signals. Over the years, these systems have seen strong development as they can enable people with limited mobility to make certain decisions to alter their environment. Additionally, their portability and ease of use have allowed a field of research to flourish for the study of cognitive and emotional processes in natural settings. The study of creativity, especially little creativity (little-c), is one example, although the results of this cutting-edge research are often poorly systematized. The purpose of the present paper, therefore, was to conduct a scoping review to describe and systematize the various studies that have been conducted on the application potential of BCI to the field of creativity. Twenty-two papers were selected that collect information on different aspects of creativity, including clinical applications; art experience in settings with high ecological validity; BCI for creative content creation, and participants’ engagement. Critical issues and potentialities of this promising area of study are also presented. Implications for future developments towards multi-brain creativity settings and C2 are discussed.

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain–computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

Iron gall inks have been among the most used writing materials after carbon black, thus representing an important element of the historical and artistic heritage of our society. Crucially, the preservation of manuscripts and drawings is influenced by the presence of these inks, leading to conservation issues related to paper degradation and text fading. Besides all the advances obtained in paper conservation, the study of iron gall ink’s behaviour and ageing is still an important topic, which requires investigation through an accurate molecular characterisation to produce reliable models. In the present work a micro-destructive method based on liquid chromatography techniques (HPLC-DAD and HPLC-ESI-Q-ToF) has been optimised starting from a model gallic acid-based ink. An in-depth study of the behaviour of the ink in time was performed by natural and artificial ageing tests, monitored by colorimetry, showing the autoxidation of gallic acid to ellagic acid in the prepared mock-ups. The effect of relative humidity on ageing processes was also evaluated, allowing us to determine different intermediates depending on the environmental conditions. Finally, the analytical method developed was then successfully applied for investigating 19th–20th century historical ink samples, where one of the identified ageing markers was detected, besides the expected gallic and ellagic acids.

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick–Prescott filter rendering a time-reversible closed-form solution. This paper also links the concept of an environmental tipping point to the statistical property of time irreversibility and assesses fourteen climate indicators. We find evidence of time irreversibility in greenhouse gas emissions, global temperature, global sea levels, sea ice area, and some natural oscillation indices. While not conclusive, our findings urge the implementation of correction policies to avoid the worst consequences of climate change and not miss the opportunity window, which might still be available, despite closing quickly.

Several studies have investigated the beneficial effects of vitamin D on survival of cancer patients. Overall evidence has been accumulating with contrasting results. This paper aims at nar-ratively reviewing the existing articles examining the link between vitamin D supplementation and cancer mortality. We performed two distinct searches to identify observational (ObS) studies and randomized clinical trials (RCTs) of vitamin D supplementation (VDS) in cancer patients and cohorts of general population, which included cancer mortality as an outcome. Published reports were gathered until March 2021. We identified 25 papers published between 2003 and 2020, including n. 8 RCTs on cancer patients, n. 8 population RCTs and n. 9 ObS studies. There was some evidence that the use of VDS in cancer patients could improve cancer survival, but no significant effect was found in population RCTs. Some ObS studies reported evidence that VDS was associated with a longer survival among cancer patients, and only one study found an opposite effect. The findings do not allow conclusive answers. VDS may have the potential as treatment to improve survival in cancer patients, but further investigations are warranted. We strongly support investment in well-designed and sufficiently powered RCTs to fully evaluate this association.

In this work, we investigate some theoretical aspects related to the estimation approach proposed by Liebermeister and Klipp, 2006, in which general rate laws, derived from standardized enzymatic mechanisms, are exploited to kinetically describe the fluxes of a metabolic reaction network, and multiple metabolic steady-state measurements are exploited to estimate the unknown kinetic parameters. Further mathematical details are deeply investigated, and necessary conditions on the amount of information required to solve the identification problem are given. Moreover, theoretical results for the parameter identifiability are provided, and symmetrical and modular properties of the proposed approach are highlighted when the global identification problem is decoupled into smaller and simpler identification problems related to the single reactions of the network. Among the advantages of the proposed innovative approach are (i) non-restrictive conditions to guarantee the solvability of the parameter estimation problem, (ii) the unburden of the usual computational complexity for such identification problems, and (iii) the ease of obtaining the required number of measurements, which are actually steady-state data, experimentally easier to obtain with respect to the time-dependent ones. A simple example concludes the paper, highlighting the mentioned advantages of the method and the implementation of the related theoretical result.

In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of (Formula presented.), by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning system. We describe the developed expert system, dubbed BN-DSSApple, and the diagnostic mechanism given the evidence provided by the user, as well as a likelihood evidence method, learned from the estimated consensus of users’ and expert’s interactions, to effectively transfer the performance of the model to different cohorts of users. Finally, we detail a novel technique for explaining the provided diagnosis, thus increasing the trust in the system. We evaluate BN-DSSApple with three different types of user studies, involving real diseased apples, where the ground truth of the target instances was established by microbiological and DNA analysis. The experiments demonstrate the performance differences in the knowledge-based reasoning mechanism due to heterogeneous users interacting with the system under various conditions and the capability of the likelihood-based method to improve the diagnostic performance in different environments.

In this paper, we experimentally investigate the effects of degradation observed on 0.15-& mu;m GaN HEMT devices when operating under realistic power amplifier conditions. The latter will be applied to the devices under test (DUT) by exploiting a low-frequency load-pull characterization technique that provides information consistent with RF operation, with the advantage of revealing electrical quantities not directly detectable at high frequency. Quantities such as the resistive gate current, play a fundamental role in the analysis of technology reliability. The experiments will be carried out on DUTs of the same periphery considering two different power amplifier operations: a saturated class-AB condition, that emphasizes the degradation effects produced by high temperatures due to power dissipation, and a class-E condition, that enhances the effects of high electric fields. The experiments will be carried out at 30 & DEG;C and 100 & DEG;C, and the results will be compared to evaluate how a specific RF condition can impact on the device degradation. Such a kind of comparison, to the authors' knowledge, has never been carried out and represents the main novelty of the present study.

As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant potential in identifying vulnerabilities; however, they are not immune to adversarial attacks. This paper presents a set of evolutionary techniques for generating adversarial instances to enhance the resilience of neural models used for vulnerability detection. The proposed approaches leverage an evolution strategy (ES) algorithm that utilizes as the fitness function the output of the neural network to deceive. By starting from existing instances, the algorithm evolves individuals, represented by source code snippets, by applying semantic-preserving transformations, while utilizing the fitness to invert their original classification. This iterative process facilitates the generation of adversarial instances that can mislead the vulnerability detection models while maintaining the original behavior of the source code. The significance of this research lies in its contribution to the field of cybersecurity by addressing the need for enhanced resilience against adversarial attacks in vulnerability detection models. The evolutionary approach provides a systematic framework for generating adversarial instances, allowing for the identification and mitigation of weaknesses in AI classifiers.

Gambling disorder (GD) is a behavioral addiction that severely impacts individuals’ functioning, leading to high socioeconomic costs. Non-invasive brain stimulation (NiBS) has received attention for treating psychiatric and neurological conditions in recent decades, but there is no recommendation for its use for GD. Therefore, this study aimed to systematically review and analyze the available literature to determine the effectiveness of NiBS in treating GD. Following the PRISMA guidelines, we screened four electronic databases up to July 2022 and selected relevant English-written original articles. We included ten papers in the systematic review and seven in the meta-analysis. As only two studies employed a sham-controlled design, the pre–post standardized mean change (SMCC) was computed as effect size only for real stimulation. The results showed a significant effect of NiBS in reducing craving scores (SMCC = −0.69; 95% CI = [−1.2, −0.2], p = 0.010). Moreover, considering the GD’s frequent comorbidity with mood disorders, we ran an exploratory analysis of the effects of NiBS on depressive symptoms, which showed significant decreases in post-treatment scores (SMCC = −0.71; 95% CI = [−1.1, −0.3], p < 0.001). These results provide initial evidence for developing NiBS as a feasible therapy for GD symptoms but further comprehensive research is needed to validate these findings. The limitations of the available literature are critically discussed.

Deep neural networks have proven to be able to learn rich internal representations, including for features that can also be used for different purposes than those the networks are originally developed for. In this paper, we are interested in exploring such ability and, to this aim, we propose a novel approach for investigating the internal behavior of networks trained for source code processing tasks. Using a simple autoencoder trained in the reconstruction of vectors representing programs (i.e., program embeddings), we first analyze the performance of the internal neurons in classifying programs according to different labeling policies inspired by real programming issues, showing that some neurons can actually detect different program properties. We then study the dynamics of the network from an information-theoretic standpoint, namely by considering the neurons as signaling systems and by computing the corresponding entropy. Further, we define a way to distinguish neurons according to their behavior, to consider them as formally associated with different abstract concepts, and through the application of nonparametric statistical tests to pairs of neurons, we look for neurons with unique (or almost unique) associated concepts, showing that the entropy value of a neuron is related to the rareness of its concept. Finally, we discuss how the proposed approaches for ranking the neurons can be generalized to different domains and applied to more sophisticated and specialized networks so as to help the research in the growing field of explainable artificial intelligence.

In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models.

Achieving a change towards the sustainable use and management of pesticides requires a multiple perspective approach that combines traditional knowledge, experience of different local stakeholders, scientific expertise, and context-specific data to provide useful and understandable information for the target farmers. In this paper, the incorporation of the information on environmental and social dimensions into a “science-based” pesticide management practice is presented as an example of a replicable multidisciplinary approach. This approach depicts the importance of the context-specific scenario analysis and of the involvement of farmers starting from their practices and their knowledge. A diverse range of engagement initiatives have been adopted to consult, inform, and involve the community. Tools as target guidelines of good practices, self-evaluation checklists, and a user-friendly indicator that considers social, environmental, and territorial parameters of the specific area, gained a lot of interest and trust and have proven to be useful in disseminating the methodology of environmental risk assessment to farmers, supporting and assisting them in the comparison of different phytosanitary strategies at farm scale to identify weaknesses in their current pesticide management at farm level and to find corresponding corrective actions. The experience also highlighted the importance of the role of properly trained and informed advisors.

Plastic pollution is an urgent worldwide environmental issue affecting marine, freshwater and terrestrial ecosystems. Half of the global plastic production is dedicated to items only used once: the so-defined single-use plastic (SUP) items. Different strategies have been implemented to reduce SUP consumption. Game-based solutions are an emerging strategy to favour behaviour change. The present systematic review aims at providing a synthesis of the current evidence about the use of game-based solutions to encourage sustainable behaviours concerning plastic (i.e., consumption, avoidance, waste management, pollution). Relevant studies were identified via three databases: Scopus, ProQuest and Web of Science for qualifying papers published between 2015 and 2021. Twenty-two studies that employed or designed game-based interventions to address the plastic problem were included. Results suggest that there is still little research exploring the use of game-based solutions to address the plastic issue. The studies included in this review mostly aim at changing behaviours and raising awareness towards plastic pollution among the general public. Although findings suggest that game-based intervention can be promising in terms of engagement and motivation and increasing knowledge of the issue, there is still little research focused on proving actual behaviour change, especially over time and in different settings.

Cu2ZnSnS4 (CZTS) is a promising absorber material to produce thin film solar cells thanks to its high absorption coefficient, low cost and low toxicity. CdS is commonly used as a buffer layer for CZTS solar cells but, beyond its toxicity, it has a nonoptimal band alignment with CZTS. ZnxSn1−xO (ZTO), based on earth-abundant and nontoxic elements and with a large and tunable band gap, is a suitable alternative buffer layer. In this paper, the atomic layer deposition (ALD) of ZTO was employed by testing different compositions and thicknesses. ALD not only leads to very compact and homogenous ZTO layers (enabling tuning the stoichiometry of the ZTO so prepared) but also makes the i-ZnO layer (usually sandwiched between the buffer layer and the transparent contact) redundant and detrimental. Through SCAPS simulation and impedance measurements, the ZnSnO/AZO interface impact on the Cd-free kesterite solar cells’ performances has been investigated, highlighting its leading role in achieving an effective charge extraction and the detrimental effect of the i-ZnO layer. With this approach, a solar cell based on an architecture simpler and more eco-friendly than the conventional one has been produced with comparable efficiencies.

To date, at least 2.41 billion people with Non-Communicable Diseases (NCDs) are in need of rehabilitation. Rehabilitation care through innovative technologies is the ideal candidate to reach all people with NCDs in need. To obtain these innovative solutions available in the public health system calls for a rigorous multidimensional evaluation that, with an articulated approach, is carried out through the Health Technology Assessment (HTA) methodology. In this context, the aim of the present paper is to illustrate how the Smart&TouchID (STID) model addresses the need to incorporate patients’ evaluations into a multidimensional technology assessment framework by presenting a feasibility study of model application with regard to the rehabilitation experiences of people living with NCDs. After sketching out the STID model’s vision and operational process, preliminary evidence on the experiences and attitudes of patients and citizens on rehabilitation care will be described and discussed, showing how they operate, enabling the co-design of technological solutions with a multi-stakeholder approach. Implications for public health are discussed including the view on the STID model as a tool to be integrated into public health governance strategies aimed at tuning the agenda-setting of innovation in rehabilitation care through a participatory methodology.

This paper presents the results obtained for the mural paintings (XV century CE) in the church of San Panfilo in Villagrande di Tornimparte (AQ, Italy) by means of noninvasive spectroscopic techniques; this research is a part of the project on the Saturnino Gatti pictorial cycle, promoted and coordinated by the AIAr (the Italian Archaeometry Association). Digital optical microscopy (OM), X-ray fluorescence spectroscopy (XRF), fiber optics reflectance spectroscopy in the UV–Vis–NIR range (FORS), Fourier transform infrared spectroscopy in the external reflection mode (ER-FTIR), and Raman spectroscopy were performed on the points selected based on the image analysis results and the few available records on previous intervention, with the aim of characterizing both the original and restoration organic and inorganic materials. The synergic application of complementary techniques allowed us to obtain a complete picture of the palette and the main alteration products and organic substances (of rather ubiquitous lipid materials and less widespread resin and proteinaceous materials in specific points). The identification of modern compounds permitted the individuation of restoration areas; this was confirmed by the comparison with multiband imaging results, as in the case of specific green and blue pigments, strictly related to the presence of high signals of zinc. This analytical protocol left only very few ambiguities and allowed to minimizing the number of samples taken to clarifying, by sample laboratory analyses, the few doubts still open.

This manuscript explores the problem of deploying sensors in networks to detect intrusions as effectively as possible. In water distribution networks, intrusions can cause a spread of contaminants over the whole network; we are searching for locations for where to install sensors in order to detect intrusion contaminations as early as possible. Monitoring epidemics can also be modelled into this framework. Given a network of interactions between people, we want to identify which “small” set of people to monitor in order to enable early outbreak detection. In the domain of the Web, bloggers publish posts and refer to other bloggers using hyperlinks. Sensors are a set of blogs that catch links to most of the stories that propagate over the blogosphere. In the sensor placement problem, we have to manage a trade-off between different objectives. To solve the resulting multi-objective optimization problem, we use a multi-objective evolutionary algorithm based on the Tchebycheff scalarization (MOEA/D). The key contribution of this paper is to interpret the weight vectors in the scalarization as probability measures. This allows us to use the Wasserstein distance to drive their selection instead of the Euclidean distance. This approach results not only in a new algorithm (MOEA/D/W) with better computational results than standard MOEA/D but also in a new design approach that can be generalized to other evolutionary algorithms.

Video games have been increasingly used as a form of therapy for various mental health conditions. Research has shown that video games can be used to treat conditions such as depression, anxiety, PTSD, and addiction. One of the main benefits of video games in therapy is that they can provide a sense of engagement and immersion that traditional therapy methods may lack. Additionally, video games can teach valuable skills such as problem solving, decision making, and coping strategies. Video games can also simulate real-life scenarios, allowing individuals to practice and improve social skills in a safe and controlled environment. Furthermore, video games can provide feedback and track progress objectively and quantifiably. This paper proposes an approach, the Video Game Therapy® (VGT®) approach, where game experience is put at the center of the therapy in a tailored way, connecting the individual patient’s personality, the therapy’s goals, and the suggested type of video game through the Myers Briggs Type Indicator (MBTI).VGT®’s core assumption is that playing video games could facilitate patients in reaching conditions where traditional methodologies and therapeutic approaches could work best. VGT® was elaborated according to the Adlerian therapy vision and, consequently, the different phases of Adlerian therapy and VGT® match. Despite the use of video games in psychotherapy might have some adverse effects in specific cases, VGT® is currently used in three associations with positive results in promoting emotional experimentation and literacy, social feeling, sense of identity, and activating cognitive processes. Future developments include expanding the use of VGT® further to validate such results from a statistical point of view.

The assessment of attention in neuropsychological patients could be performed with visual search tests. The Broken Rings enVision Search test (BReViS) here proposed represents a novel open access paper-and-pencil tool in which layout and crowding are varied among four cards. These manipulations allow the assessment of different components of attention: a selective component, the visuo-spatial orientation of attention, and the focal attention, involved in a crowding phenomenon. Our purpose was to determine the characteristics of the BReViS test, provide specific normative data, and assess these components across the lifespan. The test was administered to a sample of 550 participants aged between 20 and 79 years old and to a series of patients. Three indexes targeting different components of visuo-spatial attention (selective attention, strategic orientation of visual attention, focal attention) were obtained by combining execution times and accuracy together with the total errors. The results showed that age, education and gender influenced, in different combinations, the four indexes, for which specific norms were developed. Regression-based norms were provided in percentiles and equivalent scores. All patients showed pathological scores and specific patterns of attentional deficits. The BreViS test proved to be a free and easy valuable tool which can be used in the clinical environment to assess attentional deficits in neuropsychological patients.

This work is aimed at reviewing the current state of the art in geosite selection, assessment, and communication. We first highlight the main papers that have defined paramount concepts such as geodiversity, geoheritage, and geosites. We then delve into the theoretical principles and guidelines that have been proposed over the last twenty years by researchers who have thoroughly illustrated how to individuate and assess geosites. In doing so, we illustrate notable field examples of applications of qualitative and quantitative assessments of geosites in places such as Serbia, India, Iceland, Ecuador, Sardinia (Italy), Egypt, Tasmania (Australia), and Brazil. The third part of this work is dedicated to illustrating a list (by no means exhaustive) of works that have tried to come up with innovative tools, strategies, and solutions to promote and communicate geosites. From our work, it appears that geosites can be extremely effective as fully fledged outreach tools capable of bridging the gap between Earth science and the lay public.

DNA sequence variants (single nucleotide polymorphisms or variants, SNPs/SNVs; copy number variants, CNVs) associated to neurodevelopmental disorders (NDD) and traits often map on putative transcriptional regulatory elements, including, in particular, enhancers. However, the genes controlled by these enhancers remain poorly defined. Traditionally, the activity of a given enhancer, and the effect of its possible alteration associated to the sequence variants, has been thought to influence the nearest gene promoter. However, the obtainment of genome-wide long-range interaction maps in neural cells chromatin challenged this view, showing that a given enhancer is very frequently not connected to the nearest promoter, but to a more distant one, skipping genes in between. In this Perspective, we review some recent papers, who generated long-range interaction maps (by HiC, RNApolII ChIA-PET, Capture-HiC, or PLACseq), and overlapped the identified long-range interacting DNA segments with DNA sequence variants associated to NDD (such as schizophrenia, bipolar disorder and autism) and traits (intelligence). This strategy allowed to attribute the function of enhancers, hosting the NDD-related sequence variants, to a connected gene promoter lying far away on the linear chromosome map. Some of these enhancer-connected genes had indeed been already identified as contributive to the diseases, by the identification of mutations within the gene’s protein-coding regions (exons), validating the approach. Significantly, however, the connected genes also include many genes that were not previously found mutated in their exons, pointing to novel candidate contributors to NDD and traits. Thus, long-range interaction maps, in combination with DNA variants detected in association with NDD, can be used as “pointers” to identify novel candidate disease-relevant genes. Functional manipulation of the long-range interaction network involving enhancers and promoters by CRISPR-Cas9-based approache

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Boron containing compounds (BCCs) aroused increasing interest in the scientific community due to their wide application as drugs in various fields. In order to design new compounds hopefully endowed with pharmacological activity and also investigate their conformational behavior, the support of computational studies is crucial. Nevertheless, the suitable molecular mechanics parameterization and the force fields needed to perform these simulations are not completely available for this class of molecules. In this paper, Amber force field parameters for phenyl-, benzyl-, benzylamino-, and methylamino-boronates, a group of boron-containing compounds involved in different branches of the medicinal chemistry, were created. The robustness of the obtained data was confirmed through molecular dynamics simulations on ligand/-lactamases covalent complexes. The ligand torsional angles, populated over the trajectory frames, were confirmed by values found in the ligand geometries, located through optimizations at the DFT/B3LYP/6-31g(d) level, using water as a solvent. In summary, this study successfully provided a library of parameters, opening the possibility to perform molecular dynamics simulations of this class of boron-containing compounds.

Atherosclerosis is a chronic and progressive inflammatory process beginning early in life with late clinical manifestation. This slow pathological trend underlines the importance to early identify high-risk patients and to treat intensively risk factors to prevent the onset and/or the progression of atherosclerotic lesions. In addition to the common Cardiovascular (CV) risk factors, new markers able to increase the risk of CV disease have been identified. Among them, high levels of Lipoprotein(a)—Lp(a)—lead to very high risk of future CV diseases; this relationship has been well demonstrated in epidemiological, mendelian randomization and genome-wide association studies as well as in meta-analyses. Recently, new aspects have been identified, such as its association with aortic stenosis. Although till recent years it has been considered an unmodifiable risk factor, specific drugs have been developed with a strong efficacy in reducing the circulating levels of Lp(a) and their capacity to reduce subsequent CV events is under testing in ongoing trials. In this paper we will review all these aspects: from the synthesis, clearance and measurement of Lp(a), through the findings that examine its association with CV diseases and aortic stenosis to the new therapeutic options that will be available in the next years.

Anorexia nervosa (AN) usually emerges in adolescence when important changes occur in cognitive, emotional, and social processes. Mentalizing, alexithymia, and impulsiveness represent key dimensions for the understanding and interpretation of psychological difficulties in AN. The outbreak of the COVID-19 pandemic has impacted adolescents with AN, showing a worsening of the disease. The main aims of the present paper are (1) to compare adolescents with AN before and during the COVID-19 pandemic and (2) to explore the relationship between mentalizing, alexithymia, impulsiveness, and psychological difficulties related to eating disorders in adolescents with AN during the COVID-19 pandemic. One hundred and ninety-six AN female adolescents (N = 94 before COVID-19; N = 102 during COVID-19) participated in this study. The results show that adolescents with AN during the COVID-19 pandemic had a more impaired functioning profile than the before COVID-19 group. Mentalizing, alexithymia, and impulsiveness had a predictive role on psychological difficulties related to eating disorders in adolescents with AN during the COVID-19 pandemic. In conclusion, our data reveal that the COVID-19 pandemic has likely represented a stress condition that affects mental health; worsening the severity of adolescents with AN clinical condition. Lastly, predictive patterns suggest the existence of a link between difficulties in the ability to face the problems of the present time using effective strategies and the severity of psychological symptoms.

Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to train an agent situated in an environment how to act so as to maximize an accumulated numerical reward signal (a feedback provided by the environment to every chosen action). We explored the possibility of applying RL to pedestrian simulation. We carefully defined a reward function combining elements related to goal orientation, basic proxemics, and basic way-finding considerations. The proposed approach employs a particular training curriculum, a set of scenarios growing in difficulty supporting an incremental acquisition of general movement competences such as orientation, walking, and pedestrian interaction. The learned pedestrian behavioral model is applicable to situations not presented to the agents in the training phase, and seems therefore reasonably general. This paper describes the basic elements of the approach, the training procedure, and an experimentation within a software framework employing Unity and ML-Agents.

Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE’s pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters.

In today’s interrelated economies, financial information travel at speed of light to reach investors around the globe. Global financial markets experience regular shocks that transmit negative waves to other equity markets and different asset classes. Given the unique characteristics of exchange-traded funds (ETFs), this paper examines how different ETFs that are traded on London Financial center reacted to the Brexit event in 23 June 2016. The unexpected referendum result the day after is viewed as the next significant financial event since 2008. The paper employs an event study market model on daily and abnormal returns of the selected ETFs with respect to FTSE 250 around the event date. Contrary to what is expected, the world equities fund experienced significant positive abnormal return on the event day. Emerging markets again proved to be a preferred investment destination in times of financial turmoil; the emerging equities fund gained 3% while enjoying an 11.5% positive significant abnormal returns. The US T-Bond fund recorded a 9% return with a significant 7.2% abnormal return. The gold fund soared as much as 4% as investors seeks refuge from Brexit, and the oil fund retraced 1% amid concerns of slowing global demand.

In this paper, I discuss the role of spiritual seekers’ embodiment of karma, jnana and bhakti yoga(s) in the context of a neo-Vedantic, non-monastic ashram located in southern-Europe, an ashram I regard as an example of modern denominational yoga. Methodologically, I rely on an ex-post multi-sensory autoethnography, involving apprenticeship and full participation immersion, and I share with physical cultural studies a commitment to empirically contextualise the study of the moving body. Theoretically, I employ Shilling’s theory of the body as a multi-dimensional medium for the constitution of society, enriched by other theoretical and sensitising concepts. The findings presented in this paper show that the body of the seekers/devotees can be simultaneously framed as the source of, the location for and the means to, the constitution of the social, cultural and spiritual life of the ashram. As I discuss the development, interiorisation and implementation of serving, contemplative and devotional dispositions, which together form the scheme of dispositions that shape a yogic habitus, I also consider the ties between the specific instances under study and the more general spiritual habitus. The paper ends by broadening its focus in relation to the inclusion of Asian practices and traditions into the Western landscape

In this paper, I discuss the role of spiritual seekers’ embodiment of karma, jnana and bhakti yoga(s) in the context of a neo-Vedantic, non-monastic ashram located in southern-Europe, an ashram I regard as an example of modern denominational yoga. Methodologically, I rely on an ex-post multi-sensory autoethnography, involving apprenticeship and full participation immersion, and I share with physical cultural studies a commitment to empirically contextualise the study of the moving body. Theoretically, I employ Shilling’s theory of the body as a multi-dimensional medium for the constitution of society, enriched by other theoretical and sensitising concepts. The findings presented in this paper show that the body of the seekers/devotees can be simultaneously framed as the source of, the location for and the means to, the constitution of the social, cultural and spiritual life of the ashram. As I discuss the development, interiorisation and implementation of serving, contemplative and devotional dispositions, which together form the scheme of dispositions that shape a yogic habitus, I also consider the ties between the specific instances under study and the more general spiritual habitus. The paper ends by broadening its focus in relation to the inclusion of Asian practices and traditions into the Western landscape

Featured Application: Based on the data and analytical methodologies reported here, this review can be used as a reference for Green Stones identification and characterization protocols. Moreover, due to the multidisciplinary approach discussed, it can also be considered as an example useful for similar applications. The present review aims to discuss the importance of a multidisciplinary approach in cultural heritage and archaeometry investigations. The analytical methods used to identify and characterize “Green Stones” are discussed as an example. In the present paper, the term Green Stones is applied but not limited to jade materials, which have considerable importance in cultural heritage studies. In fact, archaeological samples made in Green Stones have been discovered worldwide, with many dating back to the Neolithic Age. Moreover, these materials represent an interesting analytical challenge, starting with their nomenclature and, in most cases, the nature of their polycrystalline samples and their heterogeneity. Indeed, after a brief introduction about the advantages of the non-destructive analytical techniques commonly used for gemstones and cultural heritage samples analyses, the limits of the same have been discussed on the basis of Green Stones applicability. Finally, a multidisciplinary methodology for Green Stones identification and full characterization, which considers materials’ heterogeneity and information, has been proposed and based on different references.

SALT, a new dedicated readout Application Specific Integrated Circuit (ASIC) for the Upstream Tracker, a new silicon detector in the Large Hadron Collider beauty (LHCb) experiment, has been designed and developed. It is a 128-channel chip using an innovative architecture comprising a low-power analogue front-end with fast pulse shaping and a 40 MSps 6-bit Analog-to-Digital Converter (ADC) in each channel, followed by a Digital Signal Processing (DSP) block performing pedestal and Mean Common Mode (MCM) subtraction and zero suppression. The prototypes of SALT were fabricated and tested, confirming the full chip functionality and fulfilling the specifications. A signal-to-noise ratio of about 20 is achieved for a silicon sensor with a 12 pF input capacitance. In this paper, the SALT architecture and measurements of the chip performance are presented.

Plastic polymer waste management is an increasingly prevalent issue. In this paper, Rhodococcus genomes were explored to predict new plastic-degrading enzymes based on recently discovered biodegrading enzymes for diverse plastic polymers. Bioinformatics prediction analyses were conducted using 124 gene products deriving from diverse microorganisms retrieved from databases, literature data, omic-approaches, and functional analyses. The whole results showed the plastic-degrading potential of Rhodococcus genus. Among the species with high plastic-degrading potential, R. erythropolis, R. equi, R. opacus, R. qingshengii, R. fascians, and R. rhodochrous appeared to be the most promising for possible plastic removal. A high number of genetic determinants related to polyester biodegradation were obtained from different Rhodococcus species. However, score calculation demonstrated that Rhodococcus species (especially R. pyridinivorans, R. qingshengii, and R. hoagii) likely possess PE-degrading enzymes. The results identified diverse oxidative systems, including multicopper oxidases, alkane monooxygenases, cytochrome P450 hydroxylases, para-nitrobenzylesterase, and carboxylesterase, and they could be promising reference sequences for the biodegradation of plastics with C−C backbone, plastics with heteroatoms in the main chain, and polyesters, respectively. Notably, the results of this study could be further exploited for biotechnological applications in biodegradative processes using diverse Rhodococcus strains and through catalytic reactions.

The determination of carbohydrate composition is extremely important for quality control in food and beverages, in material science, in pharmaceutics, and in the field of cultural heritage. Considering the complexity and the heterogeneity of the matrices, the optimization of extraction and purification steps aiming at maximizing the saccharide recovery from the matrix and effectively removing interferences is mandatory. The presence of inorganic components, besides being detrimental to the analytical instrumentation, can catalyze the isomerization of some sugars causing an alteration to their quantitative and qualitative profiles. In the present paper, protocols for suppressing the interference of inorganic ions in the quantification of monosaccharides and uronic acids by Gas Chromatography–Mass Spectrometry (GC-MS) are proposed. Two clean-up methods based on ion exchange resins (Amberlite MB-6113 and Amberlite IRN-150) and one making use of solid-phase extraction with a polypropylene Solid-Phase Extraction (SPE) column were tested on a standard carbohydrate solution, and the elution conditions optimized. The best purification conditions, in terms of higher recovery yield values for seven monosaccharides and two uronic acids, were obtained using SPE. Furthermore, the optimized SPE method was validated on a sample of mural painting rich in saccharides and inorganic material.

The advent of intra-nasal esketamine (ESK), one of the first so called fast-acting antidepressant, promises to revolutionize the management of treatment resistant depression (TRD). This NMDA receptor antagonist has proven to be rapidly effective in the short- and medium-term course of the illness, revealing its potential in targeting response in TRD. Although many TRD ESK responders are able to achieve remission, a considerable portion of them undergo a metamorphosis of their depression into different clinical presentations, characterized by instable responses and high recurrence rates that can be considered closer to the concept of Difficult to Treat Depression (DTD) than to TRD. The management of these DTD patients usually requires a further complex multidisciplinary approach and can benefit from the valuable contribution of new personalized medicine tools such as therapeutic drug monitoring and pharmacogenetics. Despite this, these patients usually come with long and complex previous treatments history and, often, advanced and sophisticated ongoing pharmacological schemes that can make the finding of new alternative options to face the current recurrences extremely challenging. In this paper, we describe two DTD patients—already receiving intranasal ESK but showing an instable course—who were clinically stabilized by the association with minocycline, a semisynthetic second-generation tetracycline with known and promising antidepressant properties.

Despite advances in our understanding of the behavioral and molecular factors that underlie the onset and maintenance of Eating Disorders (EDs), it is still necessary to optimize treatment strategies and establish their efficacy. In this context, over the past 25 years, Virtual Reality (VR) has provided creative treatments for a variety of ED symptoms, including body dissatisfaction, craving, and negative emotions. Recently, different researchers suggested that EDs may reflect a broader impairment in multisensory body integration, and a particular VR technique—VR body swapping—has been used to repair it, but with limited clinical results. In this paper, we use the results of a systematic review employing PRISMA guidelines that explore inner body perception in EDs (21 studies included), with the ultimate goal to analyze the features of multisensory impairment associated with this clinical condition and provide possible solutions. Deficits in interoception, proprioception, and vestibular signals were observed across Anorexia and Bulimia Nervosa, suggesting that: (a) alteration of inner body perception might be a crucial feature of EDs, even if further research is needed and; (b) VR, to be effective with these patients, has to simulate/modify both the external and the internal body. Following this outcome, we introduce a new therapeutic approach—Regenerative Virtual Therapy—that integrates VR with different technologies and clinical strategies to regenerate a faulty bodily experience by stimulating the multisensory brain mechanisms and promoting self-regenerative processes within the brain itself.

Assuring the safety of muscle foods and seafood is based on prerequisites and specific measures targeted against defined hazards. This concept is augmented by ‘interventions’, which are chemical or physical treatments, not genuinely part of the production process, but rather implemented in the framework of a safety assurance system. The present paper focuses on ‘Cold Atmospheric pressure Plasma’ (CAP) as an emerging non-thermal intervention for microbial decontamination. Over the past decade, a vast number of studies have explored the antimicrobial potential of different CAP systems against a plethora of different foodborne microorganisms. This contribution aims at providing a comprehensive reference and appraisal of the latest literature in the area, with a specific focus on the use of CAP for the treatment of fresh meat, fish and associated products to inactivate microbial pathogens and extend shelf life. Aspects such as changes to organoleptic and nutritional value alongside other matrix effects are considered, so as to provide the reader with a clear insight into the advantages and disadvantages of CAP-based decontamination strategies.

Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels are associated to the main drivers of the customer experience. Analyzing the samples of customer behavior only through parameters such as average and variance does not cope with the growing heterogeneity of customers. In this paper, we propose a different approach in which the samples from customer surveys are represented as discrete probability distributions whose similarities can be assessed by different models. The focus is on the Wasserstein distance, which is generally well defined, even when other distributional distances are not, and it provides an interpretable distance metric between distributions. The support of the distributions can be both one- and multi-dimensional, allowing for the joint consideration of several KPIs for each store, leading to a multi-variate histogram. Moreover, the Wasserstein barycenter offers a useful synthesis of a set of distributions and can be used as a reference distribution to characterize and classify behavioral patterns. Experimental results of real data show the effectiveness of the Wasserstein distance in providing global performance measures.

Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status is important for decision makers and citizens alike. A missile is essentially a guided rocket and therefore the term can be used to describe a very wide range of weapon systems. In this paper, we focus on long-range and intercontinental threats, which we believe are more important and problematic to defend against. We provide an overview of the two most common types of sensors, space-based infrared sensors and radars, and highlight their peculiarities and, most importantly, their drawbacks that severely limit their effectiveness.

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.

Human as well as algorithmic searches are performed to balance exploration and exploitation. The search task in this paper is the global optimization of a 2D multimodal function, unknown to the searcher. Thus, the task presents the following features: (i) uncertainty (i.e., information about the function can be acquired only through function observations), (ii) sequentiality (i.e., the choice of the next point to observe depends on the previous ones), and (iii) limited budget (i.e., a maximum number of sequential choices allowed to the players). The data about human behavior are gathered through a gaming app whose screen represents all the possible locations the player can click on. The associated value of the unknown function is shown to the player. Experimental data are gathered from 39 subjects playing 10 different tasks each. Decisions are analyzed in a Pareto optimality setting—improvement vs. uncertainty. The experimental results show that the most significant deviations from the Pareto rationality are associated with a behavior named “exasperated exploration”, close to random search. This behavior shows a statistically significant association with stressful situations occurring when, according to their current belief, the human feels there are no chances to improve over the best value observed so far, while the remaining budget is running out. To classify between Pareto and Not-Pareto decisions, an explainable/interpretable Machine Learning model based on Decision Tree learning is developed. The resulting model is used to implement a synthetic human searcher/optimizer successively compared against Bayesian Optimization. On half of the test problems, the synthetic human results as more effective and efficient.

Municipal solid waste charging schemes can be powerful drivers for local policy efforts in reaching the goals of the 2030 Agenda for Sustainable Development adopted by all United Nations Member States in 2015. This paper aims to provide empirical evidence on an economic lever to meet SDG 11, Target 11.6 and SDG 12, Target 12.5 by applying the polluter-pays principle to waste management that also depends on user engagement through modern incentivizing charging systems. Unit pricing schemes in municipal solid waste management are often associated with a higher percentage of separated waste, less per capita waste production, and reduced service costs. We checked whether unit pricing schemes and the percentage of the sorted waste collection were correlated, assessed whether there is an impact on per capita waste generation, examined the impact on the total cost of management, and explored how specific phases of waste management were affected. The analysis was based on an empirical sample of 1,636 municipalities, of which 506 had unit pricing schemes in place. Our results confirm that unit pricing schemes can be associated with a higher percentage of sorted waste collection and less per capita waste generation. The impact of unit pricing on the total cost of management was not found to be significant, probably due to different impacts on specific services and phases of waste management. The policy implications are as follows: it is suggested that public administrators put data-driven policy targets into government programs that are applied at an operational level by competent municipal civil servants and codified into single programming documents for contracting waste management utilities according to SDG 11 Target 11.6 and SDG 12 Target 12.5.