Determinants of the Use of Safety Restraint Systems in Italy

Wearing a safety restraint system is one of the most effective measures to substantially reduce the risk of serious or fatal accidents. Despite their benefits, a survey in 2015 revealed that on average 62 out of 100 Italian front car occupants wore their seat belt and only 15% of the rear seat passengers were regularly wearing their seat belt. According to several studies, one's (driving) behaviour is based on a combination of attitudes toward the behaviour, subjective norm and perceived behavioural control. The present study aims at understanding factors contributing to the low wearing rates in Italy. The data used are based on a questionnaire survey carried out among a representative sample of more than 1.000 Italian drivers within the ESRA project (European Survey of Road users' safety Attitudes). The survey involved 17 European countries and covered several themes on (un)safe traffic behaviour and attitudes among which those related to the use of seat belts and child restraint systems. Two methods of investigation were adopted: the comparison between the Italian situation with the European best performers, pointing out the relevant difference with the included selected European Countries, and the use of regression models to study the association between several explanatory variables and self-declared behaviours related to the use of safety restraint systems. The main results show a high acceptability of risky behaviour in Italy and a relevant contribution of age and gender in shaping attitudes towards unsafe traffic behaviours. A number of recommendations are proposed to change people's unsafe behaviour and attitudes in Italy, providing both enforcement and voluntary (e.g. campaigns, education and training, incentives) measures

The association between national culture, road safety performance, and support for road safety policy measures

There are considerable differences between countries when it comes to road safety performance, as indicated by the number of road fatalities per 100,000 inhabitants. These discrepancies are strongly associated with differences in wealth and prosperity, as expected, but are also related to national culture. The overall objective of this exploratory study is to identify relationships between national culture, road safety performance and public support for policy measures. Using the revised version of Hofstede's cultural dimensions, we show the strong correlation between national culture and road safety performance, which exists even after controlling for the national level of wealth as measured by the gross national income. Furthermore, by combining the national cultural dimensions with data on 29 countries from the second stage of ESRA, the E-Survey of Road users' Attitudes, this study demonstrates that culture also affects the level of public support for policy measures related to road safety. Specifically, for many measures, the degree of individualism accounts for a considerable part of the statistical variation in the public support for policy measures across countries—except for those measures for which the support is very high in most countries. Possible explanations are given for the seemingly paradoxical finding that countries which witness high resistance to road safety policy measures have nevertheless managed to achieve better road safety performance

Improving sustainable mobility in university campuses. The case study of Sapienza University

The pursue of sustainable mobility is one of the greatest environmental challenges nowadays. It requires a people mind shift, where the use of private vehicles give way to different modes of public transport like buses, bicycles, car sharing, electric cars, and walking lanes. This new call to make mobility sustainable has already been undertaken by policymakers and public managers in many urban contexts around the world, as well as, more recently, by the managers of university systems. The paper shows the work developed in 2018 for the Sapienza Sustainable University Mobility Plan (SUMP). The study stems from the need to understand and improve, in the sustainability direction, modes of travel for the students and staff of one of the oldest universities in the world, and one of the largest in Europe (112,142 students enrolled and 23,101 between academic staff and no academic staff), with its premises located in a complex and challenging urban context such as the city of Rome. The SUMP has been developed in two phases. The first one investigated travel patterns and the reasons for the modal shift and highlighted the main issues. The second phase defined strategies and interventions to be implemented in the short, medium, and long term to make students and staff's mobility more environmentally sustainable. The methodology used in the fact-finding stage was the online survey that was carried out through the use of a diversified questionnaire for staff and students of the University. The sample of students who participated in the survey amounted to 14,719 units, while the sample of faculty and staff was 9,403. The main questionnaire outcomes showed that the attitudes recorded were largely different between faculty and staff and students. While for the first ones the choice of private vehicles is the first option (36%), for students public transport is the prevailing preference (78%). According to the critical aspects found in this first stage, the SUMP objectives were defined, leading to the identification of macro-areas of intervention and specific actions. At a policy and strategic level, the attention was focused on the guidelines issued by the United Nations, the European Commission, and the Network of Universities for Sustainable Development, of which Sapienza University is a member. For this reason, the identification of strategies and interventions results from the combination of the first phase analysis, the Sapienza Governance objectives, and the national and international context in which the SUMP was drafted. Five macro-areas of intervention have been identified: Smart Strategies, Pedestrian Mobility, Cycling, Local Public Transport, Private Transport, and for each one specific intervention to be implemented in different time frames have been defined

Translating proteomic into functional data: An high mobility group A1 (HMGA1) proteomic signature has prognostic value in breast cancer

Cancer is a very heterogeneous disease, and biological variability adds a further level of complexity, thus limiting the ability to identify new genes involved in cancer development. Oncogenes whose expression levels control cell aggressiveness are very useful for developing cellular models that permit differential expression screenings in isogenic contexts. HMGA1 protein has this unique property because it is a master regulator in breast cancer cells that control the transition from a nontumorigenic epithelial-like phenotype toward a highly aggressive mesenchymal-like one. The proteins extracted from HMGA1-silenced and control MDA-MB-231 cells were analyzed using label-free shotgun mass spectrometry. The differentially expressed proteins were cross-referenced with DNA microarray data obtained using the same cellular model and the overlapping genes were filtered for factors linked to poor prognosis in breast cancer gene expression meta-data sets, resulting in an HMGA1 protein signature composed of 21 members (HRS, HMGA1 reduced signature). This signature had a prognostic value (overall survival, relapse-free survival, and distant metastasis-free survival) in breast cancer. qRT-PCR, Western blot, and immunohistochemistry analyses validated the link of three members of this signature (KIFC1, LRRC59, and TRIP13) with HMGA1 expression levels both in vitro and in vivo and wound healing assays demonstrated that these three proteins are involved in modulating tumor cell motility. Combining proteomic and genomic data with the aid of bioinformatic tools, our results highlight the potential involvement in neoplastic transformation of a restricted list of factors with an as-yet-unexplored role in cancer. These factors are druggable targets that could be exploited for the development of new, targeted therapeutic approaches in triple-negative breast cancer

HMGA1 is a novel downstream nuclear target of the insulin receptor signaling pathway

High-mobility group AT-hook 1 (HMGA1) protein is an important nuclear factor that activates gene transcription by binding to AT-rich sequences in the promoter region of DNA. We previously demonstrated that HMGA1 is a key regulator of the insulin receptor (INSR) gene and individuals with defects in HMGA1 have decreased INSR expression and increased susceptibility to type 2 diabetes mellitus. In addition, there is evidence that intracellular regulatory molecules that are employed by the INSR signaling system are involved in post-translational modifications of HMGA1, including protein phosphorylation. It is known that phosphorylation of HMGA1 reduces DNA-binding affinity and transcriptional activation. In the present study, we investigated whether activation of the INSR by insulin affected HMGA1 protein phosphorylation and its regulation of gene transcription. Collectively, our findings indicate that HMGA1 is a novel downstream target of the INSR signaling pathway, thus representing a new critical nuclear mediator of insulin action and function

Chromatin Immunoprecipitation to Analyze DNA Binding Sites of HMGA2

BACKGROUND: HMGA2 is an architectonic transcription factor abundantly expressed during embryonic and fetal development and it is associated with the progression of malignant tumors. The protein harbours three basically charged DNA binding domains and an acidic protein binding C-terminal domain. DNA binding induces changes of DNA conformation and hence results in global overall change of gene expression patterns. Recently, using a PCR-based SELEX (Systematic Evolution of Ligands by Exponential Enrichment) procedure two consensus sequences for HMGA2 binding have been identified. METHODOLOGY/PRINCIPAL FINDINGS: In this investigation chromatin immunoprecipitation (ChIP) experiments and bioinformatic methods were used to analyze if these binding sequences can be verified on chromatin of living cells as well. CONCLUSION: After quantification of HMGA2 protein in different cell lines the colon cancer derived cell line HCT116 was chosen for further ChIP experiments because of its 3.4-fold higher HMGA2 protein level. 49 DNA fragments were obtained by ChIP. These fragments containing HMGA2 binding sites have been analyzed for their AT-content, location in the human genome and similarities to sequences generated by a SELEX study. The sequences show a significantly higher AT-content than the average of the human genome. The artificially generated SELEX sequences and short BLAST alignments (11 and 12 bp) of the ChIP fragments from living cells show similarities in their organization. The flanking regions are AT-rich, whereas a lower conservation is present in the center of the sequences

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub