Should we be aiming to engage drivers more with others on-road? Driving moral disengagement and self-reported driving aggression

Aggressive driving behaviours may be associated with greater crash risk in situations where drivers engage in riskier types of behaviours such as following too closely. It also appears that many drivers who do not normally regard themselves as angry or aggressive report engaging in aggressive driving acts. Qualitative studies have suggested that drivers explain these behaviours with reference to justified retaliation or beliefs that such acts ‘teach’ other drivers a ‘lesson’ or to exercise better driving manners or etiquette. Drivers may also argue that their behaviour does not have a negative impact on others. Such descriptions of motives bear a strong resemblance to the psychological mechanisms of moral disengagement. Moral disengagement is where individuals detach themselves from their usual self-regulatory processes or morality in order to behave in ways that run counter to their normal moral standards. Moral disengagement offers a potential explanation of how apparently ‘good’ or moral people commit ‘bad’ or immoral behaviours. Categories of moral disengagement are: cognitively misinterpreting the behaviour (e.g euphemistic labelling); disconnecting with the target (e.g. attributing blame to the target); and distorting or denying the impact of the behaviour. An on-line survey with a convenience sample of general drivers (N = 294) was used to explore the potential utility of moral disengagement in explaining self-reported driving aggression over and above the explanatory power provided by constructs that are normally associated with self-reported on-road aggression. Hierarchical regression analysis was used with measures of trait anger, driving anger (DAS), moral disengagement, and driving moral disengagement (an adaptation of the measure of moral disengagement for the driving context). Results revealed that the independent variables together explained 37% of the variation in self-reported driving aggression (as measured by the Driving Anger Expression scale, DAX). Driving moral disengagement was a significant predictor of driving aggression (p < .001) after accounting for the contribution of age, gender, driving anger, and moral disengagement. Moreover, inspection of the beta weights suggested that driving moral disengagement (beta = .57) was the strongest predictor for this sample, accounting for 20% of the unique variance in driving aggression (sr2 = .20). The pattern of results suggests drivers with higher tendencies to morally disengage in the driving context may respond to others more aggressively on-road. Moreover, driving moral disengagement appeared to add to our understanding of why some angry drivers do not respond aggressively on-road while others do. Seeking to prevent drivers from activating moral disengagement while driving may be worthy of exploration as a way of reducing non-violent, yet potentially still risky, forms of driving aggression

The Natural Products Atlas : an open access knowledge base for microbial natural products discovery

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries

Development and preliminary validation of a scale of driving moral disengagement as a tool in the exploration of driving aggression

Aggressive driving has been found to result in road collisions which are a major cause of injury, fatality and financial cost in motorised countries. Qualitative and survey based studies suggest that drivers use justifications or explanations of their aggressive driving that bear strong resemblance to Bandura’s mechanisms of moral disengagement. The aim of the current study was to explore the applicability of moral disengagement to the driving context using a purpose-adapted scale, the Driving Moral Disengagement Scale. A convenience sample of general drivers (N = 294) responded to an on-line survey comprised of measures of trait anger, driving anger (DAX revised), moral disengagement and driving moral disengagement. Factor analysis allowed for reduction of the new scale from 23 items to 13 items, and this shortened Driving Moral Disengagement Scale (DMDS) had good internal reliability (Cronbach’s alpha = .83). Scree plot criteria indicated a one factor solution accounting for 34.34% of the variance. Bivariate correlations on the shortened DMDS revealed significant and positive relationships with measures of driving aggression, moral disengagement, trait anger and driving anger, r = .28–.55. Moreover the strength of the association between driving aggression and moral disengagement was greater than that with driving anger. Hierarchical regression revealed driving moral disengagement as the strongest significant predictor of driving aggression, accounting for 18% of the unique variation in the DV, and suggesting this may be a more useful predictor than driving anger. In addition, significant differences between participants’ mean scores for moral disengagement in everyday situations and their driving moral disengagement scores support the interpretation that drivers may behave differently from their ‘usual’ selves when driving, and that the driving context may encourage both greater moral disengagement and greater tendency towards aggressive responses. Chi square analysis indicated that those who scored high on driving moral disengagement were significantly more likely to report aggressive responses to driving situations than those with low driving moral disengagement scores (with a large effect size, φ = .42). This suggests that the DMDS may be useful for future driving aggression research. Implications for intervention are that aiming to alert drivers to their usual self-censure mechanisms or to prevent the tendency to moral disengagement while driving may be effective in reducing driving aggression and the risky or dangerous responses associated with it on road

The Natural Products Atlas - data download

Download files from the Natural Products Atlas (npatlas.org). van Santen, J. A.; Jacob, G.; Leen Singh, A.; Aniebok, V.; Balunas, M. J.; Bunsko, D.; Carnevale Neto, F.; Castaño-Espriu, L.; Chang, C.; Clark, T. N.; Cleary Little, J. L.; Delgadillo, D. A.; Dorrestein, P. C.; Duncan, K. R.; Egan, J. M.; Galey, M. M.; Haeckl, F. P. J.; Hua, A.; Hughes, A. H.; Iskakova, D.; Khadilkar, A.; Lee, J.-H.; Lee, S.; LeGrow, N.; Liu, D. Y.; Macho, J. M.; McCaughey, C. S.; Medema, M. H.; Neupane, R. P.; O’Donnell, T. J.; Paula, J. S.; Sanchez, L. M.; Shaikh, A. F.; Soldatou, S.; Terlouw, B. R.; Tran, T. A.; Valentine, M.; van der Hooft, J. J. J.; Vo, D. A.; Wang, M.; Wilson, D.; Zink, K. E.; Linington, R. G.* "The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery”, ACS Central Science, 2019, 5, 11, 1824-1833. 10.1021/acscentsci.9b00806 Now includes ontological data from: NP Classifier - https://npclassifier.ucsd.edu/ ClassyFire - http://classyfire.wishartlab.com/ Including archived versions, extra data download types, and new MIBiG and GNPS IDs Includes dump of compounds deemed out of scope and removed from DB on May 19, 2021. The latest versions (v2021_08 onward) include the ontological data in the full JSON download

The Natural Products Atlas - data download

Download files from the Natural Products Atlas (npatlas.org). van Santen, J. A.; Jacob, G.; Leen Singh, A.; Aniebok, V.; Balunas, M. J.; Bunsko, D.; Carnevale Neto, F.; Castaño-Espriu, L.; Chang, C.; Clark, T. N.; Cleary Little, J. L.; Delgadillo, D. A.; Dorrestein, P. C.; Duncan, K. R.; Egan, J. M.; Galey, M. M.; Haeckl, F. P. J.; Hua, A.; Hughes, A. H.; Iskakova, D.; Khadilkar, A.; Lee, J.-H.; Lee, S.; LeGrow, N.; Liu, D. Y.; Macho, J. M.; McCaughey, C. S.; Medema, M. H.; Neupane, R. P.; O’Donnell, T. J.; Paula, J. S.; Sanchez, L. M.; Shaikh, A. F.; Soldatou, S.; Terlouw, B. R.; Tran, T. A.; Valentine, M.; van der Hooft, J. J. J.; Vo, D. A.; Wang, M.; Wilson, D.; Zink, K. E.; Linington, R. G.* "The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery”, ACS Central Science, 2019, 5, 11, 1824-1833. 10.1021/acscentsci.9b00806 Now includes ontological data from: NP Classifier - https://npclassifier.ucsd.edu/ ClassyFire - http://classyfire.wishartlab.com/ Including archived versions, extra data download types, and new MIBiG and GNPS IDs Includes dump of compounds deemed out of scope and removed from DB on May 19, 2021. The latest versions (v2021_08 onward) include the ontological data in the full JSON download