Streptomyces axinellae sp. nov., isolated from the Mediterranean sponge Axinella polypoides (Porifera)

An actinomycete strain, isolated from the marine sponge Axinella polypoides collected from Banyuls-sur-Mer, France, was characterized using a polyphasic approach. Based on its chemotaxonomic and morphological characteristics, strain Pol001(T) belongs to the genus Streptomyces. The strain is characterized by ll-diaminopimelic acid in the cell wall, menaquinones MK-9(H(4), H(6), H(8)) and a DNA G+C content of 71.0 mol%. It forms a separate phyletic line based on phylogenetic analyses of the nearly complete 16S rRNA gene sequence. Strain Pol001(T) could be differentiated from other closely related Streptomyces species with validly published names by phenotypic and genotypic analysis. DNA-DNA hybridization between strain Pol001(T) and closely related reference strains further confirmed that strain Pol001(T) represents a novel taxon of the genus Streptomyces. Therefore, it is proposed that strain Pol001(T) represents a novel species in the genus Streptomyces, Streptomyces axinellae sp. nov.; the type strain is Pol001(T) (=DSM 41948(T) =CIP 109838(T))

Anti-Parasitic Compounds from Streptomyces sp. Strains Isolated from Mediterranean Sponges

Actinomycetes are prolific producers of pharmacologically important compounds accounting for about 70% of the naturally derived antibiotics that are currently in clinical use. In this study, we report on the isolation of Streptomyces sp. strains from Mediterranean sponges, on their secondary metabolite production and on their screening for anti-infective activities. Bioassay-guided isolation and purification yielded three previously known compounds namely, cyclic depsipeptide valinomycin, indolocarbazole alkaloid staurosporine and butenolide. This is the first report of the isolation of valinomycin from a marine source. These compounds exhibited novel anti-parasitic activities specifically against Leishmania major (valinomycin IC50 < 0.11 μM; staurosporine IC50 5.30 μM) and Trypanosoma brucei brucei (valinomycin IC50 0.0032 μM; staurosporine IC50 0.022 μM; butenolide IC50 31.77 μM). These results underscore the potential of marine actinomycetes to produce bioactive compounds as well as the re-evaluation of previously known compounds for novel anti-infective activities

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive

Pandemic Boredom: Little Evidence That Lockdown-Related Boredom Affects Risky Public Health Behaviors Across 116 Countries

Some public officials have expressed concern that policies mandating collective public health behaviors (e.g., national/regional "lockdown ") may result in behavioral fatigue that ultimately renders such policies ineffective. Boredom, specifically, has been singled out as one potential risk factor for noncompliance. We examined whether there was empirical evidence to support this concern during the COVID-19 pandemic in a large cross-national sample of 63,336 community respondents from 116 countries. Although boredom was higher in countries with more COVID-19 cases and in countries that instituted more stringent lockdowns, such boredom did not predict longitudinal within-person decreases in social distancing behavior (or vice versa; n = 8,031) in early spring and summer of 2020. Overall, we found little evidence that changes in boredom predict individual public health behaviors (handwashing, staying home, self-quarantining, and avoiding crowds) over time, or that such behaviors had any reliable longitudinal effects on boredom itself. In summary, contrary to concerns, we found little evidence that boredom posed a public health risk during lockdown and quarantine

Structure of Dark Triad Dirty Dozen Across Eight World Regions

The Dark Triad (i.e., narcissism, psychopathy, Machiavellianism) has garnered intense attention over the past 15 years. We examined the structure of these traits’ measure—the Dark Triad Dirty Dozen (DTDD)—in a sample of 11,488 participants from three W.E.I.R.D. (i.e., North America, Oceania, Western Europe) and five non-W.E.I.R.D. (i.e., Asia, Middle East, non-Western Europe, South America, sub-Saharan Africa) world regions. The results confirmed the measurement invariance of the DTDD across participants’ sex in all world regions, with men scoring higher than women on all traits (except for psychopathy in Asia, where the difference was not significant). We found evidence for metric (and partial scalar) measurement invariance within and between W.E.I.R.D. and non-W.E.I.R.D. world regions. The results generally support the structure of the DTDD

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies

Gender gap in parental leave intentions: Evidence from 37 countries

This is the final version. Available from Wiley via the DOI in this record. Despite global commitments and efforts, a gender-based division of paid and unpaid work persists. To identify how psychological factors, national policies, and the broader sociocultural context contribute to this inequality, we assessed parental-leave intentions in young adults (18–30years old) planning to have children (N = 13,942; 8,880 identified as women; 5,062 identified as men) across 37 countries that varied in parental-leave policies and societal gender equality. In all countries, women intended to take longer leave than men. National parental-leave policies and women’s political representation partially explained cross-national variations in the gender gap. Gender gaps in leave intentions were paradoxically larger in countries with more gender-egalitarian parental-leave policies (i.e., longer leave available to both fathers and mothers). Interestingly, this cross-national variation in the gender gap was driven by cross-national variations in women’s (rather than men’s) leave intentions. Financially generous leave and gender-egalitarian policies (linked to men’s higher uptake in prior research) were not associated with leave intentions in men. Rather, men’s leave intentions were related to their individual gender attitudes. Leave intentions were inversely related to career ambitions. The potential for existing policies to foster gender equality in paid and unpaid work is discussed.SSHRC Insight Development GrantSSHRC Insight GrantEconomic and Social Research CouncilState Research AgencyGuangdong 13th-five Philosophy and Social Science Planning ProjectNational Natural Science Foundation of ChinaSwiss National Science FoundationSwiss National Science FoundationCenter for Social Conflict and Cohesion StudiesCenter for Intercultural and Indigenous ResearchSSHRC Postdoctoral FellowshipSlovak Research and Development AgencySwiss National Science FoundationCanada Research ChairsSocial Sciences and Humanities Research Council of CanadaOntario Ministry of Research and InnovationHSE University, RFFaculty of Arts, Masaryk Universit

.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant