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Abstract Background An intronic deletion within intron 2 of the DCDC2 gene encompassing the entire READ1 (hereafter, READ1d) has been associated in both children with developmental dyslexia (DD) and typical readers (TRs), with interindividual variation in reading performance and motion perception as well as with structural and functional brain alterations. Visual motion perception -- specifically processed by the magnocellular (M) stream -- has been reported to be a solid and reliable endophenotype of DD. Hence, we predicted that READ1d should affect neural activations in brain regions sensitive to M stream demands as reading proficiency changes. Methods We investigated neural activations during two M-eliciting fMRI visual tasks (full-field sinusoidal gratings controlled for spatial and temporal frequencies and luminance contrast, and sensitivity to motion coherence at 6%, 15% and 40% dot coherence levels) in four subject groups: children with DD with/without READ1d, and TRs with/without READ1d. Results At the Bonferroni-corrected level of significance, reading skills showed a significant effect in the right polar frontal cortex during the full-field sinusoidal gratings-M task. Regardless of the presence/absence of the READ1d, subjects with poor reading proficiency showed hyperactivation in this region of interest (ROI) compared to subjects with better reading scores. Moreover, a significant interaction was found between READ1d and reading performance in the left frontal opercular area 4 during the 15% coherent motion sensitivity task. Among subjects with poor reading performance, neural activation in this ROI during this specific task was higher for subjects without READ1d than for READ1d carriers. The difference vanished as reading skills increased. Conclusions Our findings showed a READ1d-moderated genetic vulnerability to alterations in neural activation in the ventral attentive and salient networks during the processing of relevant stimuli in subjects with poor reading proficiency.

The present study investigates the adoption of predictive methods for road safety in the Italian context. The main goal of predictive methods is to describe safety performances of a road infrastructure according to the real number of occurred crashes and the actual infrastructure characteristics. Currently, the most reliable and widely used model is reported in the Highway Safety Manual (HSM), initially developed for the United States. In order to adapt the HSM existing model to the Italian context a calibration procedure is required. The main purpose of this study is to conduct the sensitivity analysis of the calibration factor, to evaluate the influence of various road characteristics (expressed in terms of Crash Modification Factors – CMFs) on the reliability of the model. Results show that the most influential factors are represented by planimetric curve radius, followed by shoulder type and width and Roadside Hazard Rating (RHR). In addition, the Italian roads taken into account for the present case study, which are single carriageway rural roads located in Emilia Romagna region, have generally exhibited more critical conditions compared to American roads. As a consequence, CMFs expressing the aforementioned infrastructure characteristics are typically greater than 1. Furthermore, road features and driving habits vary significantly with the orographic context (mountain, hill, or valley) and geographical area, factors that cannot be neglected when calibrating predictive models. As a result, a layered calibration would bring to the most reliable solution and, in specific situations, alternative predictive models to HSM could be developed.

Due to repeated load cycles and climate impacts, road pavement deteriorates. One of the primary causes of pavement degradation is the formation of rutting under the wheel path on the road surface. Rutting's impact on vehicle performance, especially on rainy days (since the rain would fill the rutting depression), leads to longer braking distances compared to dry conditions. This study developed a MATLAB-based model for calculating vehicle braking distance on wet asphalt pavement affected by rutting, using dynamic skid resistances generated through Back-Propagation Neural Network (BPNN) analysis. This study addresses the worst-case scenario in which rutting is filled with water, then calculates the required vehicle braking distance under various Water Film Thickness (WFT) conditions. The developed model can perform these evaluations for different operational conditions across various input ranges, such as precipitation intensity, number of lanes, lane width, cross slope, average texture depth, rutting depths, and accumulated WFT. As an outcome, the vehicle braking distance can be estimated as a function of Rutting Depth (RD) within a known vehicle speed interval. After validating the proposed model against existing approaches from the literature, several sensitivity analyses are conducted to assess the impact of influencing parameters on the results. Moreover, the study examines the relationship between AASHTO braking distance requirements and the RD threshold levels adopted by several highway agencies. Furthermore, this model is also applicable to real-world case studies, enabling the calculation of vehicle braking distances with varying RDs in the presence of various WFTs on the pavement surface.

The adhesive properties of tack coats between asphalt pavement layers are crucial for the pavement’s structural behavior. This study first involved numerical analyses to compare stress patterns, deformations, and displacements in the pavement structure under various geometric and mechanical conditions. A rational calculation method based on the theory of elastic multilayer systems was used to quantify the impact of layer properties such as thickness, stiffness modulus, and Poisson’s ratio on interlayer bonding. Three bonding conditions—Full Friction, Partial Bonding, and Full Debonding—were analyzed to understand the tack coat’s effect between the top two layers. The second phase involved characterizing the mechanical behavior of the interface through shear strength tests (Leutner shear test) on both laboratory-prepared specimens and samples from a 10-year-old highway. Specimens were prepared using a Roller Compactor and tested under different interface conditions: hot-on-hot (H/H), residual bitumen 200 g/m2 (RB 200), and residual bitumen 400 g/m2 (RB 400). The tests examined the bonding effects in terms of tangential force and shear displacement at failure, as well as the impact of vehicular traffic on rutting and fatigue failure. Finally, this study investigated the long-term aging effects of the binder on interlayer bonding and sought to correlate the results of numerical calculations with those of the laboratory tests.

In this paper, the effectiveness of reinforcements for flexible pavements is evaluated through an analysis of reflective cracking. Different stiffness and thickness reinforcements are considered for the rehabilitation of an already cracked pavement. The effect of the reinforcement is assessed from two different perspectives: (i) the ability to reduce stresses in the rehabilitated pavement layers, and (ii) the capacity to mitigate the crack propagation from deeper layers. A finite element model (FEM) is adopted to study the stress and strain state of the pavement layers. The pavement model has been properly validated, transitioning from a simply supported beam scheme to an elastic multilayer model. In addition, to represent crack propagation, fracture evolution is analyzed using Linear Elastic Fracture Mechanics (LEFMs) and Paris’ law. The effect of different reinforcements on the pavement is then simulated. The results show that the reinforcement performance is strictly dependent on the interlayer thickness and stiffness. In particular, high stiffness reinforcements (geomembranes) show increasing effectiveness with stiffness, both in terms of reflective cracking and stress reduction. Conversely, low stiffness reinforcements (SAMIs) show a variable trend with the stiffness modulus. In fact, extremely low stiffness is effective in slowing down crack propagation but is detrimental to the wearing course’s stress condition. However, as the stiffness increases, the likelihood of cracking in the wearing course decreases, though only a small beneficial effect is registered for crack propagation in the base layer.

This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years.

A deficit in short-term memory (STM) functions characterizes many neurodevelopmental disorders, in particular, specific learning disorders. Hence, there is a need to develop a web-based platform capable of testing specific variables and administration conditions in a controlled manner. The platform herein presented allows for the assessment of short-term memory (STM) items and order components in a series of different conditions. Stimulus types, presentation, and response modalities were appropriately selected to assess the impact of those variables on memory performances. The usefulness of such a systematic, fine-grained analysis of STM functions was tested by applying the complete assessment in a group of 100 school-age children (47 Typically Developing children and 53 children with learning disorders) and evaluating the capacity of the software to highlight different specific memory processes activated during reading, writing, and calculation. A cluster analysis was applied to the learning performances of the whole group, and a four-cluster solution representing the best division into subgroups of learning disorders (affecting reading, writing, and mathematical skills, variously combined) also showed clear-cut differences in the children’s STM profiles. This confirms the potential and the usefulness of the tool for the characterization of STM in school-age children.

The purpose of this study is to determine the safety effectiveness of an intervention on an existing road by using predictive methods. Predictive methods allow the benefit of the intervention to be quantified in terms of crash reduction. Currently, the most widely used model is reported in the Highway Safety Manual, developed in the US. The HSM model is adapted to the Italian context through a calibration procedure. The model is then applied to two future scenarios: in the absence and presence of intervention. The redesign intervention consists of rehabilitating some road sections and constructing five tunnel bypasses to avoid crossing residential areas. The comparison between the ‘with’ and ‘without’ scenario estimated an overall reduction in the number of accidents of around 45%. The variant scenario is based on reasonable assumptions that allowed the determination of the proportion of traffic that will be diverted to the variant. In addition, several alternative future scenarios are considered to assess a possible different trend in assumed traffic distribution. Moreover, a possible overall increase or reduction in total traffic affecting the road is taken into account. The results showed that the intervention provided significant benefits even with increased traffic, proving the resilience of the intervention.

This paper focuses on the gut-lung axis in the context of Inflammatory Bowel Disease (IBD) and Chronic Obstructive Pulmonary Disease (COPD), highlighting the key role played by microbial dysbiosis and the impact of environmental and genetic factors on the innate and acquired immune system and on chronic inflammation in the intestinal and pulmonary tracts. Recent evidence indicates that Antigen-Presenting Cells (APCs) perform regulatory activity influencing the composition of the microbiota. APCs (macrophages, dendritic cells, B cells) possess membrane receptors known as Pattern Recognition Receptors (PRRs), a category of toll-like receptors (TLRs). PRRs recognise distinct microbial structures and microbial metabolites called Signals, which modulate the saprophytic microbial equilibrium of the healthy microbiota by recognising molecular profiles associated with commensal microbes (Microbe-Associated Molecular Patterns, MAMPs). During dysbiosis, pathogenic bacteria can prompt an inflammatory response, producing PAMPs (Pathogen-Associated Molecular Patterns) thereby activating the proliferation of inflammatory response cells, both local and systemic. This series of regulatory and immune-response events is responsible (together with chronic infection, incorrect diet, obesity, etc.) for the systemic chronic inflammation (SCI) known as 'low-grade inflammation' typical of COPD and IBD. This review looks at immunological research and explores the role of the microbiota, looking at two recent clinical studies, SPIROMICS and AERIS. There is a need for further clinical studies to characterize the pulmonary microbiota and to obtain new information about the pathogenesis of lung disease to improve our knowledge and treatment strategies and identify new therapeutic targets., Author(s): Francesco De Nuccio [sup.1], Prisco Piscitelli [sup.2], Domenico Maurizio Toraldo [sup.3] Author Affiliations: (1) grid.9906.6, 0000 0001 2289 7785, Laboratory Human Anatomy, Department of Biological and Environmental Sciences and [...]

The use of Electric Vehicles (EV) seems to be a promising solution to achieve a sustainable road transport system. Among the contactless dynamic vehicle charging technologies, the use of Charging Units (CUs)—cement concrete box—embedded into the road pavement seems to be a favorable option. The available scientific papers related to the structural effects of embedding CUs in road pavements consider the CU as a solid box, even if a cavity is needed for the electrical technologies’ accommodation. This is why the current research is aimed at studying electrified roads (e-road) with different CU cavity shapes and dimensions. In detail, pavement structural responses are investigated, as a first step, adopting a Finite Element Model (FEM), and, as a second step, the long-term performances (fatigue cracking/rutting proneness) are evaluated. The study is divided into two phases: the theoretical fatigue/rutting assessment, which allows to calculate the critical load repetitions leading to pavement failure, and an urban case study with the goals of both assessing the theoretical results and computing fatigue/rutting performance with real scale traffic conditions. The outcomes demonstrate that CUs can be used with no significant impacts on the long-term road pavement structural performance, laying the foundation for a future upgrading of the existing urban road networks.