Is sexism a gender issue? A motivated social cognition perspective on men’s and women’s sexist attitudes toward own and other gender

The present research investigated the antecedents of ambivalent sexism (i.e., hostile and benevolent forms) in both men and women toward own and other gender. In two heterogeneous adult samples (Study 1: N = 179 and Study 2: N = 222), it as revealed that gender itself was only a minor predictor of sexist attitudes compared to the substantial impact of individual differences in general motivated cognition (i.e., Need for closure). Analyses further showed that the relationship between Need for closure and sexism was mediated by social attitudes (i.e., right-wing authoritarianism and social dominance orientation), which were differently related to benevolent and hostile forms of sexism. In the discussion it is argued that sexism primarily stems from individual differences in motivated cognitive style, which relates to peoples? perspective on the social world, rather than from group differences between men and women

Direct contact and authoritarianism as moderators between extended contact and reduced prejudice: Lower threat and greater trust as mediators

Using a representative sample of Dutch adults (N = 1238), we investigated the moderating influence of direct contact and authoritarianism on the potential of extended contact to reduce prejudice. As expected, direct contact and authoritarianism moderated the effect of extended contact on prejudice. Moreover, the third-order moderation effect was also significant, revealing that extended contact has the strongest effect among high authoritarians with low levels of direct contact. We identified trust and perceived threat as the mediating processes underlying these moderation effects. The present study thus attests to the theoretical and practical relevance of reducing prejudice via extended contact. The discussion focuses on the role of extended contact in relation to direct contact and authoritarianism as well as on the importance of trust in intergroup contexts

Spectral, crystallographic, theoretical and antibacterial studies of palladium(II)/platinum(II) complexes with unsymmetric diphosphine ylides

The reaction of alpha-keto-stabilized diphosphine ylides [Ph2P(CH2)(n)PPh2C(H)C(O)C6H4-p-CN] (n = 1 (Y-1); n = 2 (Y-2)) with dibromo(1,5-cyclooctadiene) palladium(II)/platinum(II) complexes, [Pd/PtBr2(cod)], in equimolar ratio gave the new cyclometalated Pd(II) and Pt(II) complexes [Br2Pd(kappa(2)-Y-1)] (1), [Br2Pt(kappa(2)-Y-1)] (2), [Br2Pd(kappa(2)-Y-2)] (3) and [Br2Pt(kappa(2)-Y-2)] (4). These compounds were screened in a search for novel antibacterial agents and characterized successfully using Fourier transfer infrared and NMR (H-1, C-13 and P-31) spectroscopic methods. Also, the structures of complexes 1 and 2 were characterized using X-ray crystallography. The results showed that the P,C-chelated complexes 1 and 2 have structures consisting of five-membered rings, while 3 and 4 have six-membered rings, formed by coordination of the ligand through the phosphine group and the ylidic carbon atom to the metal centre. Also, a theoretical study of the structures of complexes 1-4 was conducted at the BP86/def2-SVP level of theory. The nature of metal-ligand bonds in the complexes was investigated using energy decomposition analyses (EDA) and extended transition state combined with natural orbitals for chemical valence analyses. The results of EDA confirmed that the main portions of Delta E-int, about 57-58%, in the complexes are allocated to Delta E-elstat

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection

Churn modeling is important to sustain profitable customer relationships in saturated consumer markets. A churn model predicts the likelihood of customer defection. This is important to target retention offers to the right customers and to use marketing resources efficiently. The prevailing approach toward churn model development, supervised learning, suffers an important limitation: it does not allow the marketing analyst to account for campaign planning objectives and constraints during model building. Our key proposition is that creating a churn model in awareness of actual business requirements increases the performance of the final model for marketing decision support. To demonstrate this, we propose a decision-centric framework to create churn models. We test our modeling framework on eight real-life churn data sets and find that it performs significantly better than state-of-the-art churn models. Further analysis suggests that this improvement comes directly from incorporating business objectives into model building, which confirms the effectiveness of the proposed framework. In particular, we estimate that our approach increases the per customer profits of retention campaigns by $.47 on average

Does lower cognitive ability predict greater prejudice?

Historically, leading scholars proposed a theoretical negative association between cognitive abilities and prejudice. Until recently, however, the field has been relatively silent on this topic, citing concerns with potential confounds (e.g., education levels). Instead, researchers focused on other individual-difference predictors of prejudice, including cognitive style, personality, negativity bias, and threat. Yet there exists a solid empirical paper trail demonstrating that lower cognitive abilities (e.g., abstract-reasoning skills and verbal, nonverbal, and general intelligence) predict greater prejudice. We discuss how the effects of lower cognitive ability on prejudice are explained (i.e., mediated) by greater endorsement of right-wing socially conservative attitudes. We conclude that the field will benefit from a recognition of, and open discussion about, differences in cognitive abilities between those lower versus higher in prejudice. To advance the scientific discussion, we propose the Cognitive Ability and Style to Evaluation model, which outlines the cognitive psychological underpinnings of ideological belief systems and prejudice

The interplay between cognitive risk and resilience factors in remitted depression: A network analysis

Individuals in remission from depression are at increased risk for developing future depressive episodes. Several cognitive risk- and resilience factors have been suggested to account for this vulnerability. In the current study we explored how risk- and protective factors such as cognitive control, adaptive and maladaptive emotion regulation, residual symptomatology, and resilience relate to one another in a remitted depressed (RMD) sample. We examined the relationships between these constructs in a cross-sectional dataset of 69 RMD patients using network analyses in order to obtain a comprehensive, data-driven view on the interplay between these constructs. We subsequently present an association network, a concentration network, and a relative importance network. In all three networks resilience formed the central hub, connecting perceived cognitive control (i.e., working memory complaints), emotion regulation, and residual symptomatology. The contribution of the behavioral measure for cognitive control in the network was negligible. Moreover, the directed relative importance network indicates bidirectional influences between these constructs, with all indicators of centrality suggesting a key role of resilience in remission from depression. The presented findings are cross-sectional and networks are limited to a fixed set of key constructs in the literature pertaining cognitive vulnerability for depression. These findings indicate the importance of resilience to successfully cope with stressors following remission from depression. Further in-depth studies will be essential to identify the specific underlying resilience mechanisms that may be key to successful remission from depression

Does virulence assessment of Vibrio anguillarum using sea bass (Dicentrarchus labrax) larvae correspond with genotypic and phenotypic characterization?

Background: Vibriosis is one of the most ubiquitous fish diseases caused by bacteria belonging to the genus Vibrio such as Vibrio (Listonella) anguillarum. Despite a lot of research efforts, the virulence factors and mechanism of V. anguillarum are still insufficiently known, in part because of the lack of standardized virulence assays. Methodology/Principal Findings: We investigated and compared the virulence of 15 V. anguillarum strains obtained from different hosts or non-host niches using a standardized gnotobiotic bioassay with European sea bass (Dicentrarchus labrax L.) larvae as model hosts. In addition, to assess potential relationships between virulence and genotypic and phenotypic characteristics, the strains were characterized by random amplified polymorphic DNA (RAPD) and repetitive extragenic palindromic PCR (rep-PCR) analyses, as well as by phenotypic analyses using Biolog's Phenotype MicroArray (TM) technology and some virulence factor assays. Conclusions/Significance: Virulence testing revealed ten virulent and five avirulent strains. While some relation could be established between serotype, genotype and phenotype, no relation was found between virulence and genotypic or phenotypic characteristics, illustrating the complexity of V. anguillarum virulence. Moreover, the standardized gnotobiotic system used in this study has proven its strength as a model to assess and compare the virulence of different V. anguillarum strains in vivo. In this way, the bioassay contributes to the study of mechanisms underlying virulence in V. anguillarum

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. The rapid and accurate quantification of the strength of these interactions (as measured by binding affinity) is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient-specific medicine. Recent evidence suggests that molecular dynamics (MD) can achieve useful predictive accuracy (< 1 kcal/mol). For this predictive accuracy to impact clinical decision making, binding free energy computational campaigns must provide results rapidly and without loss of accuracy. This demands advances in algorithms, scalable software systems, and efficient utilization of supercomputing resources. We introduce a framework called HTBAC, designed to support accurate and scalable drug binding affinity calculations, while marshaling large simulation campaigns. We show that HTBAC supports the specification and execution of free-energy protocols at scale. This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -- a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution