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Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

This systematic review summarizes the evidence about toe walking (TW) interventions in persons with autism. Following the PRISMA guidelines, a systematic search of MEDLINE, CINAHL, PsycINFO, The Cochrane Library, Google Scholar, and Opengrey was performed. Nine articles (all case reports or case series) were included. Methodological quality was assessed using the Mayo Evidence-Based Practice Centre tool. The included studies considered 17 subjects (16 males; age range: 4-15 years). All studies reported a reduction of TW frequency, but the follow-up was lacking in seven studies. There is a lack of high-quality studies with a sufficiently large and well-characterized sample to assess the effectiveness of TW interventions in autistic persons. These findings strongly support the need for further research in this area.

Abstract Background Defective mitochondria and aberrant brain mitochondrial bioenergetics are consistent features in syndromic intellectual disability disorders, such as Rett syndrome (RTT), a rare neurologic disorder that severely affects mainly females carrying mutations in the X-linked MECP2 gene. A pool of CB1 cannabinoid receptors (CB1R), the primary receptor subtype of the endocannabinoid system in the brain, is located on brain mitochondrial membranes (mtCB1R), where it can locally regulate energy production, synaptic transmission and memory abilities through the inhibition of the intra-mitochondrial protein kinase A (mtPKA). In the present study, we asked whether an overactive mtCB1R-mtPKA signaling might underlie the brain mitochondrial alterations in RTT and whether its modulation by systemic administration of the CB1R inverse agonist rimonabant might improve bioenergetics and cognitive defects in mice modeling RTT. Methods Rimonabant (0.3 mg/kg/day, intraperitoneal injections) was administered daily to symptomatic female mice carrying a truncating mutation of the Mecp2 gene and its effects on brain mitochondria functionality, systemic oxidative status, and memory function were assessed. Results mtCB1R is overexpressed in the RTT mouse brain. Subchronic treatment with rimonabant normalizes mtCB1R expression in RTT mouse brains, boosts mtPKA signaling, and restores the defective brain mitochondrial bioenergetics, abnormal peripheral redox homeostasis, and impaired cognitive abilities in RTT mice. Limitations The lack of selectivity of the rimonabant treatment towards mtCB1R does not allow us to exclude that the beneficial effects exerted by the treatment in the RTT mouse model may be ascribed more broadly to the modulation of CB1R activity and distribution among intracellular compartments, rather than to a selective effect on mtCB1R-mediated signaling. The low sample size of few experiments is a further limitation that has been addressed replicating the main findings under different experimental conditions. Conclusions The present data identify mtCB1R overexpression as a novel molecular alteration in the RTT mouse brain that may underlie defective brain mitochondrial bioenergetics and cognitive dysfunction.

Abstract Background Electroencephalography (EEG) is a promising tool for identifying the physiological biomarkers of fibromyalgia (FM). Evidence suggests differences in power band and density between individuals with FM and healthy controls. EEG changes appear to be associated with pain intensity; however, their relationship with the quality of pain has not been examined. We aimed to investigate whether abnormal EEG in the frontal and central points of the 10–20 EEG system in individuals with FM is associated with pain’s sensory-discriminative and affective-motivational dimensions. The association between EEG and the two dimensions of emotional disorders (depression and anxiety) was also investigated. Methods In this cross-sectional pilot study, pain experience (pain rating index [PRI]) and two dimensions of emotional disorders (depression and anxiety) were assessed using the McGill Pain Questionnaire (PRI-sensory and PRI-affective) and Hospital Anxiety and Depression Scale (HADS), respectively. In quantitative EEG analysis, the relative spectral power of each frequency band (delta, theta, alpha, and beta) was identified in the frontal and central points during rest. Results A negative correlation was found between the relative spectral power for the delta bands in the frontal (r= -0.656; p = 0.028) and central points (r= -0.624; p = 0.040) and the PRI-affective scores. A positive correlation was found between the alpha bands in the frontal (r = 0.642; p = 0.033) and central points (r = 0.642; p = 0.033) and the PRI-affective scores. A negative correlation between the delta bands in the central points and the anxiety subscale of the HADS (r = -0.648; p = 0.031) was detected. Conclusion The affective-motivational dimension of pain and mood disorders may be related to abnormal patterns of electrical activity in patients with FM. Trial registration Retrospectively registered on ClinicalTrials.gov (NCT05962658).

Abstract Background and objectives The Mediterranean Diet (MD) has been recognized for its benefits for human health and sustainability for the planet, but it has considered not easy to reproduce in other populations. The United Nations Educational, Scientific and Cultural Organization (UNESCO) Chair on Health Education and Sustainable Development is fostering a research project (Planeterranea), aiming to identify a healthy dietary pattern based on local foods with the same MD features. The aim of our study is to develop a MD-based food pyramid for Asian populations. Methods Asia was stratified into six areas according to pedo-climatic conditions. For each region a comprehensive scoping review of local crops and typical foods was conducted on several databases such as the US Department of Agriculture (USDA)’s database, the Food and Agriculture Organization of the United Nations (FAO) website, and PubMed, focusing on both plant-based and animal-based foods. Narrative review was then conducted on the identified foods to determine their nutritional composition and planetary health impact. Finally, the collected information was used to build up the Asian food pyramid with details for each respective region. Results We proposed a food pyramid for Asian countries, guaranteeing the same nutritional intake and health benefits as MD, by considering dietary habits and typical foods of this population. From the bottom to the top, Asian fruits and vegetables present similar nutritional profile as those in MD. Whole grains (barley) may represent valid alternative to white rice. Sesame oil represents a source of unsaturated fats and an alternative to olive oil. Legumes (soybean), edible insects, mushrooms and algae, guarantee an adequate intake of plant-based proteins with a complete amino-acid profile and a low environmental impact with respect to animal-based ones. Conclusions This work is a new insight of healthy and sustainable local food system based on MD principles for the Asian population.

The occurrence of pericardial rupture as a complication of blunt thoracic trauma has the potential to result in cardiac dislocation unless promptly identified and subjected to suitable surgical intervention. This phenomenon is exceptionally uncommon and is associated with a significant mortality rate between 30 and 67%. The diagnosis remains complex due to the scarcity of symptoms. However, the routine application of computed tomography (CT) scans in the management of trauma patients could facilitate the early identification of cardiac dislocation. In this case report, we described the diagnostic–therapeutic pathway of a pericardial rupture due to high-intensity trauma.

Abstract Background Despite extensive studies on the neurobiological correlates of traumatic brain injury (TBI), little is known about its molecular determinants on long-term consequences, such as dementia and Alzheimer’s disease (AD). Methods Here, we carried out behavioural studies and an extensive biomolecular analysis, including inflammatory cytokines, gene expression and the combination of LC-HRMS and MALDI-MS Imaging to elucidate the targeted metabolomics and lipidomics spatiotemporal alterations of brains from wild-type and APP-SWE mice, a genetic model of AD, at the presymptomatic stage, subjected to mild TBI. Results We found that brain injury does not affect cognitive performance in APP-SWE mice. However, we detected an increase of key hallmarks of AD, including Aβ1-42 levels and BACE1 expression, in the cortices of traumatized transgenic mice. Moreover, significant changes in the expanded endocannabinoid (eCB) system, or endocannabinoidome (eCBome), occurred, including increased levels of the endocannabinoid 2-AG in APP-SWE mice in both the cortex and hippocampus, and N-acylserotonins, detected for the first time in the brain. The gene expression of enzymes for the biosynthesis and inactivation of eCBs and eCB-like mediators, and some of their main molecular targets, also underwent significant changes. We also identified the formation of heteromers between cannabinoid 1 (CB1) and serotonergic 2A (5HT2A) receptors, whose levels increased in the cortex of APP-SWE mTBI mice, possibly contributing to the exacerbated pathophysiology of AD induced by the trauma. Conclusions Mild TBI induces biochemical changes in AD genetically predisposed mice and the eCBome may play a role in the pathogenetic link between brain injury and neurodegenerative disorders also by interacting with the serotonergic system.

In the wake of the COVID-19 pandemic, the surveillance and safety measures of indoor Cultural Heritage sites have become a paramount concern due to the unique challenges posed by their enclosed environments and high visitor volumes. This communication explores the integration of Artificial Intelligence (AI) in enhancing epidemiological surveillance and health safety protocols in these culturally significant spaces. AI technologies, including machine learning algorithms and Internet of Things (IoT) sensors, have shown promising potential in monitoring air quality, detecting pathogens, and managing crowd dynamics to mitigate the spread of infectious diseases. We review various applications of AI that have been employed to address both direct health risks and indirect impacts such as visitor experience and preservation practices. Additionally, this paper discusses the challenges and limitations of AI deployment, such as ethical considerations, privacy issues, and financial constraints. By harnessing AI, Cultural Heritage sites can not only improve their resilience against future pandemics but also ensure the safety and well-being of visitors and staff, thus preserving these treasured sites for future generations. This exploration into AI’s role in post-COVID surveillance at Cultural Heritage sites opens new frontiers in combining technology with traditional conservation and public health efforts, providing a blueprint for enhanced safety and operational efficiency in response to global health challenges.

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

School board trustees play an important role in the education of children throughout Ontario. Using an online survey of Ontario residents, expectations of school board trustees are explored in detail. The survey included an open-ended question that asked respondents what role they see being performed by trustees, as well as a question that asked respondents whether they preferred a "delegate" governing model, which believes trustees should represent constituents, or a "trustee" model, which believes trustees should render decisions based on their best judgement. On the surface, respondents see three primary and distinct roles: represent the public; support the administrative functions of the school board, and to ensure educational outcomes are met. In addition, the sample tilts favourably towards the "delegate" governing model. Regression models identify some factors that help explain respondents' choices.

Despite promise of its supplementation as both monotherapy and add-on treatment in autism spectrum disorder (ASD), the biobehavioral effects of Palmitoylethanolamide (PEA) in autistic adults have never been explored so far. We discussed the cases of two autistic adults with different degrees of severity (level 1 and level 2) presenting with symptoms of psychic distress, who were treated with ultramicronized-PEA (um-PEA) 600 mg/day monotherapy for a sustained period of 4 months. The level 1 autistic patient showed improved depressive symptoms and social engagement at a 12-week follow-up, in parallel to a tendency toward reduced inflammatory response and enhanced endocannabinoid (eCB) signaling, partially relapsing after um-PEA discontinuation at four months. Opposedly, the level 2 autistic patient exhibited a generally stable psychosocial functioning for the initial 12 weeks, consistent with basically unchanged immune and eCBs levels, abruptly deteriorating and leading to antipsychotic initiation afterwards. No significant side effects were reported in both cases during the observation period. The two cases suggest that um-PEA could be an effective option for the treatment of psychic distress in level 1 autistic adults, warranting further investigation of its age- and level-specificity and of the biological underpinnings of its therapeutic effect in ASD.

Phase I clinical trials represent a critical point in drug development because the investigational medicinal product is being tested in humans for the first time. For this reason, it is essential to evaluate and identify the Maximum Tolerated Dose (MTD) and the safety of the new compound. To mitigate the possible risks associated with drug administration and treatment, the European Competent Authority issued various guidelines to provide provisions and harmonize risk management processes. In the UK and Italy, particular attention should be paid to the Medicines & Healthcare Products Regulatory Agency (MHRA) phase I accreditation scheme and the specific rules set by the Italian Drug Authority through the AIFA Determination no. 809/2015. Both reference documents are based on the concept of quality risk management while conducting phase I clinical studies. Moreover, the AIFA determination outlines specific requirements for those sites that want to conduct non-profit phase I clinical trials. Indeed, the document reports peculiar activities to the “Clinical Trial Quality Team”, which is a team that should support the clinical site researchers in designing, starting, performing, and closing non-profit phase I studies. In this paper, we provide a general overview of the main European guidelines concerning the management of risks during phase I trials, focusing on the main peculiarities of the schemes and rules set by the MHRA and AIFA.

Exploring the ‘fragile crust of our planet’ is crucial for human survival holding an immense social and economic significance. Therefore, innovative approaches become of utmost importance for obtaining precise subsoil models in urban areas making the latter more resilient to natural disasters. Due to logistic issues and a high level of anthropogenic disturbance and related background noise, urban areas are usually intrinsically more problematic for applying geophysical prospecting methods. This work presents the results obtained by Deep Electrical Resistivity Tomography and P wave Seismic Reflection surveys performed along the Ferrara, north Italy, city walls documenting the adaptability of the geophysical surveys and how it is possible to obtain high-quality electrical resistivity and seismic data even in complex urban settings. The joint interpretation of geoelectrical and seismic data fully integrated with tectonic, geological and hydrogeological information allowed to reconstruct the stratigraphic evolution down to a depth of about 1.5 km. These results highlight the occurrence of a syndepositional Quaternary tectonic tilting associated with the growth of a fault-propagation fold.

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. The results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.